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Peach R, Friedrich M, Fronemann L, Muthuraman M, Schreglmann SR, Zeller D, Schrader C, Krauss JK, Schnitzler A, Wittstock M, Helmers AK, Paschen S, Kühn A, Skogseid IM, Eisner W, Mueller J, Matthies C, Reich M, Volkmann J, Ip CW. Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning. NPJ Digit Med 2024; 7:160. [PMID: 38890413 PMCID: PMC11189529 DOI: 10.1038/s41746-024-01140-6] [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: 09/19/2023] [Accepted: 05/22/2024] [Indexed: 06/20/2024] Open
Abstract
Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches.
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Affiliation(s)
- Robert Peach
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany.
- Department of Brain Sciences, Imperial College London, London, UK.
| | - Maximilian Friedrich
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, USA
- Harvard Medical School, Boston, USA
| | - Lara Fronemann
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | | | | | - Daniel Zeller
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Christoph Schrader
- Department of Neurology and Clinical Neurophysiology, Hannover Medical School, Hannover, Germany
| | - Joachim K Krauss
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Ann-Kristin Helmers
- Department of Neurology, UKSH, Kiel Campus Christian-Albrechts-University, Kiel, Germany
| | - Steffen Paschen
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Andrea Kühn
- Department of Neurology, Movement Disorder and Neuromodulation Unit, Charité - Universitätsmedizin, Berlin, Germany
| | - Inger Marie Skogseid
- Movement Disorders Unit, Department of Neurology, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Wilhelm Eisner
- Department of Neurology, Innsbruck Medical University, 6020, Innsbruck, Austria
| | - Joerg Mueller
- Klinik für Neurologie mit Stroke Unit, Vivantes Klinikum Spandau, Berlin, Germany
| | - Cordula Matthies
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Martin Reich
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany
| | - Chi Wang Ip
- Department of Neurology, University Hospital Würzburg, Würzburg, 97080, Germany.
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Singleton SP, Velidi P, Schilling L, Luppi AI, Jamison K, Parkes L, Kuceyeski A. Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use elucidated via network control theory. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00139-3. [PMID: 38839036 DOI: 10.1016/j.bpsc.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/03/2024] [Accepted: 05/18/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Heavy alcohol use (HAU) and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. METHODS Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. RESULTS We find that individuals with HAU had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. CONCLUSIONS This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use.
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Affiliation(s)
- S Parker Singleton
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A..
| | - Puneet Velidi
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, U.S.A
| | - Louisa Schilling
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Andrea I Luppi
- Montreal Neurological Institute, McGill University, Montreal, CA
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
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Dong C, Thalamuthu A, Jiang J, Mather KA, Sachdev PS, Wen W. Brain structural covariances in the ageing brain in the UK Biobank. Brain Struct Funct 2024; 229:1165-1177. [PMID: 38625555 PMCID: PMC11147885 DOI: 10.1007/s00429-024-02794-4] [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: 07/18/2023] [Accepted: 03/21/2024] [Indexed: 04/17/2024]
Abstract
The morphologic properties of brain regions co-vary or correlate with each other. Here we investigated the structural covariances of cortical thickness and subcortical volumes in the ageing brain, along with their associations with age and cognition, using cross-sectional data from the UK Biobank (N = 42,075, aged 45-83 years, 53% female). As the structural covariance should be estimated in a group of participants, all participants were divided into 84 non-overlapping, equal-sized age groups ranging from the youngest to the oldest. We examined 84 cortical thickness covariances and subcortical covariances. Our findings include: (1) there were significant differences in the variability of structural covariance in the ageing process, including an increased variance, and a decreased entropy. (2) significant enrichment in pairwise correlations between brain regions within the occipital lobe was observed in all age groups; (3) structural covariance in older age, especially after the age of around 64, was significantly different from that in the youngest group (median age 48 years); (4) sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations showed significant associations with age. These trends varied, with some correlations strengthening, some weakening, and some reversing in direction with advancing age. Additionally, as ageing was associated with cognitive decline, most of the correlations with cognition displayed an opposite trend compared to age associated patterns of correlations.
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Affiliation(s)
- Chao Dong
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia.
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
- Neuropsychiatric Institute (NPI), Prince of Wales Hospital, Randwick, NSW, 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW, Sydney, Australia
- Neuropsychiatric Institute (NPI), Prince of Wales Hospital, Randwick, NSW, 2031, Australia
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Miraglia F, Pappalettera C, Barbati SA, Podda MV, Grassi C, Rossini PM, Vecchio F. Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice. Brain Commun 2024; 6:fcae137. [PMID: 38741663 PMCID: PMC11089417 DOI: 10.1093/braincomms/fcae137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/22/2023] [Accepted: 05/07/2024] [Indexed: 05/16/2024] Open
Abstract
Stroke is one of the leading causes of disability worldwide. There are many different rehabilitation approaches aimed at improving clinical outcomes for stroke survivors. One of the latest therapeutic techniques is the non-invasive brain stimulation. Among non-invasive brain stimulation, transcranial direct current stimulation has shown promising results in enhancing motor and cognitive recovery both in animal models of stroke and stroke survivors. In this framework, one of the most innovative methods is the bihemispheric transcranial direct current stimulation that simultaneously increases excitability in one hemisphere and decreases excitability in the contralateral one. As bihemispheric transcranial direct current stimulation can create a more balanced modulation of brain activity, this approach may be particularly useful in counteracting imbalanced brain activity, such as in stroke. Given these premises, the aim of the current study has been to explore the recovery after stroke in mice that underwent a bihemispheric transcranial direct current stimulation treatment, by recording their electric brain activity with local field potential and by measuring behavioural outcomes of Grip Strength test. An innovative parameter that explores the complexity of signals, namely the Entropy, recently adopted to describe brain activity in physiopathological states, was evaluated to analyse local field potential data. Results showed that stroke mice had higher values of Entropy compared to healthy mice, indicating an increase in brain complexity and signal disorder due to the stroke. Additionally, the bihemispheric transcranial direct current stimulation reduced Entropy in both healthy and stroke mice compared to sham stimulated mice, with a greater effect in stroke mice. Moreover, correlation analysis showed a negative correlation between Entropy and Grip Strength values, indicating that higher Entropy values resulted in lower Grip Strength engagement. Concluding, the current evidence suggests that the Entropy index of brain complexity characterizes stroke pathology and recovery. Together with this, bihemispheric transcranial direct current stimulation can modulate brain rhythms in animal models of stroke, providing potentially new avenues for rehabilitation in humans.
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Affiliation(s)
- Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
| | - Saviana Antonella Barbati
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Maria Vittoria Podda
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Claudio Grassi
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
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Golestani AM. Editorial for "Associations of Brain Entropy Estimated by Resting State fMRI With Physiological Indices, Body Mass Index, and Cognition". J Magn Reson Imaging 2024; 59:1708-1709. [PMID: 37667467 DOI: 10.1002/jmri.28997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/06/2023] Open
Affiliation(s)
- Ali M Golestani
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
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Penalba-Sánchez L, Silva G, Crook-Rumsey M, Sumich A, Rodrigues PM, Oliveira-Silva P, Cifre I. Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2811. [PMID: 38732917 PMCID: PMC11086092 DOI: 10.3390/s24092811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/20/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024]
Abstract
Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.
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Affiliation(s)
- Lucía Penalba-Sánchez
- Facultat de Psicología, Ciències de l’Educació i de l’Esport (FPCEE), Blanquerna, Universitat Ramon Llull, 08022 Barcelona, Spain; (L.P.-S.)
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculty of Education and Psychology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
- Department of Psychology, Nottingham Trent University (NTU), Nottingham NG1 4FQ, UK
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke-University Magdeburg (OVGU), 39120 Magdeburg, Germany
| | - Gabriel Silva
- Centro de Biotecnologia e Química Fina (CBQF)—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Mark Crook-Rumsey
- UK Dementia Research Institute (UK DRI), Centre for Care Research and Technology, Imperial College London, London W1T 7NF, UK
- UK Dementia Research Institute (UK DRI), Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RX, UK
| | - Alexander Sumich
- Department of Psychology, Nottingham Trent University (NTU), Nottingham NG1 4FQ, UK
| | - Pedro Miguel Rodrigues
- Centro de Biotecnologia e Química Fina (CBQF)—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Patrícia Oliveira-Silva
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculty of Education and Psychology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Ignacio Cifre
- Facultat de Psicología, Ciències de l’Educació i de l’Esport (FPCEE), Blanquerna, Universitat Ramon Llull, 08022 Barcelona, Spain; (L.P.-S.)
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Lin C, Lee SH, Huang CM, Wu YW, Chang YX, Liu HL, Ng SH, Cheng YC, Chiu CC, Wu SC. Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial. J Affect Disord 2024; 351:15-23. [PMID: 38281596 DOI: 10.1016/j.jad.2024.01.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD. METHODS A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes. RESULTS A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels. LIMITATIONS Sample size is small with only marginal clinical effect. CONCLUSION These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression.
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Affiliation(s)
- Chemin Lin
- Department of Psychiatry, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan; College of Medicine, Chang Gung University, Taoyuan County, Taiwan.; Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Keelung, Taiwan
| | - Shwu-Hua Lee
- College of Medicine, Chang Gung University, Taoyuan County, Taiwan.; Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan County, Taiwan
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Wen Wu
- Department of Engineering and System Science, National Tsing Hua University, Hsinchu, Taiwan
| | - You-Xun Chang
- Department of Engineering and System Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Shu-Hang Ng
- Department of Head and Neck Oncology Group, Linkou Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan; Department of Diagnostic Radiology, Linkou Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Ying-Chih Cheng
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Psychiatry, China Medical University Hsinchu Hospital, China Medical University, Hsinchu, Taiwan; Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Chih-Chiang Chiu
- Department of Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan; Department of Psychiatry, Taipei Medical University, Taipei, Taiwan.
| | - Shun-Chi Wu
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
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8
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Rhodes LJ, Borghetti L, Morris MB. Multiscale entropy in a 10-minute vigilance task. Int J Psychophysiol 2024; 198:112323. [PMID: 38428744 DOI: 10.1016/j.ijpsycho.2024.112323] [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: 12/13/2023] [Revised: 02/20/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024]
Abstract
Research has shown multiscale entropy, brain signal behavior across time scales, to reliably increase at lower time scales with time-on-task fatigue. However, multiscale entropy has not been examined in short vigilance tasks (i.e., ≤ 10 min). Addressing this gap, we examine multiscale entropy during a 10-minute Psychomotor Vigilance Test (PVT). Thirty-four participants provided neural data while completing the PVT. We compared the first 2 min of the task to the 7th and 8th minutes to avoid end-spurt effects. Results suggested increased multiscale entropy at lower time scales later compared to earlier in the task, suggesting multiscale entropy is a strong marker of time-on-task fatigue onset during short vigils. Separate analyses for Fast and Slow performers reveal differential entropy patterns, particularly over visual cortices. Here, observed brain-behavior linkage between entropy and reaction time for slow performers suggests that entropy assays over sensory cortices might have predictive value for fatigue onset or shifts from on- to off-task states.
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Affiliation(s)
- L Jack Rhodes
- Ball Aerospace at Wright-Patterson Air Force Base, OH, United States of America.
| | - Lorraine Borghetti
- Air Force Research Laboratory, Wright-Patterson Air Force Base, OH, United States of America
| | - Megan B Morris
- Air Force Research Laboratory, Wright-Patterson Air Force Base, OH, United States of America
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Singer B, Meling D, Hirsch-Hoffmann M, Michels L, Kometer M, Smigielski L, Dornbierer D, Seifritz E, Vollenweider FX, Scheidegger M. Psilocybin enhances insightfulness in meditation: a perspective on the global topology of brain imaging during meditation. Sci Rep 2024; 14:7211. [PMID: 38531905 PMCID: PMC10966054 DOI: 10.1038/s41598-024-55726-x] [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: 08/19/2022] [Accepted: 02/27/2024] [Indexed: 03/28/2024] Open
Abstract
In this study, for the first time, we explored a dataset of functional magnetic resonance images collected during focused attention and open monitoring meditation before and after a five-day psilocybin-assisted meditation retreat using a recently established approach, based on the Mapper algorithm from topological data analysis. After generating subject-specific maps for two groups (psilocybin vs. placebo, 18 subjects/group) of experienced meditators, organizational principles were uncovered using graph topological tools, including the optimal transport (OT) distance, a geometrically rich measure of similarity between brain activity patterns. This revealed characteristics of the topology (i.e. shape) in space (i.e. abstract space of voxels) and time dimension of whole-brain activity patterns during different styles of meditation and psilocybin-induced alterations. Most interestingly, we found that (psilocybin-induced) positive derealization, which fosters insightfulness specifically when accompanied by enhanced open-monitoring meditation, was linked to the OT distance between open-monitoring and resting state. Our findings suggest that enhanced meta-awareness through meditation practice in experienced meditators combined with potential psilocybin-induced positive alterations in perception mediate insightfulness. Together, these findings provide a novel perspective on meditation and psychedelics that may reveal potential novel brain markers for positive synergistic effects between mindfulness practices and psilocybin.
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Affiliation(s)
- Berit Singer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
| | - Daniel Meling
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Department of Psychosomatic Medicine and Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - Matthias Hirsch-Hoffmann
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, University Hospital Zurich, Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Michael Kometer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Lukasz Smigielski
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Dario Dornbierer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Franz X Vollenweider
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
| | - Milan Scheidegger
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Department of Neuroradiology, University Hospital Zurich, Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
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10
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Xin X, Yu J, Gao X. The brain entropy dynamics in resting state. Front Neurosci 2024; 18:1352409. [PMID: 38595975 PMCID: PMC11002175 DOI: 10.3389/fnins.2024.1352409] [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: 12/08/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.
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Affiliation(s)
- Xiaoyang Xin
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
- Preschool College, Luoyang Normal University, Luoyang, China
| | - Jiaqian Yu
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
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11
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Stobbe E, Forlim CG, Kühn S. Impact of exposure to natural versus urban soundscapes on brain functional connectivity, BOLD entropy and behavior. ENVIRONMENTAL RESEARCH 2024; 244:117788. [PMID: 38040180 DOI: 10.1016/j.envres.2023.117788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND Humans have been moving from rural to urban environments for decades. This process may have important consequences for our health and well-being. Most previous studies have focused on visual input, and the auditory domain has been understudied so far. Therefore, we set out to investigate the influence of exposure to natural vs urban soundscapes on brain activity and behavior. METHODS Resting-state fMRI data was acquired while participants (N = 35) listened to natural and urban soundscapes. Two affective questionnaires (the Positive and Negative Affect Schedule (PANAS) and the Perceived Stress Scale) and two cognitive tasks (dual n-back (DNB) and the backward digit-span (BDS)) were assessed before and after each soundscape condition. To quantify brain function we used complexity and network measures, namely brain entropy (BEN) and whole brain functional connectivity (FC). To study the link between brain and behavior, changes in BEN and whole brain FC were correlated to changes in cognitive performance and self-reported affect. RESULTS We found higher BEN when listening to urban sounds in posterior cingulate gyrus, cuneus and precuneus, occipital lobe/calcarine as compared to nature sounds, which was negatively correlated to (post-pre) differences in positive affect (PANAS) in the urban soundscape condition. In addition, we found higher FC between areas in the auditory, cinguloopercular, somatomotor hand and mouth networks when listening to nature as compared to urban sounds which was positively correlated to (post-pre) differences of the of the composite score of Digit span and N-back for nature soundscape. CONCLUSIONS This study provides a framework for the neural underpinnings of how natural versus urban soundscapes affect both whole brain FC and BEN and bear implications for the understanding of how the physical auditory environment affects brain function and subsequently observed behavior. Moreover, correlations with cognition and affect reveal the meaning that exposure to soundscapes may have on the human brain. To the best of our knowledge this is the first study to analyze BEN and whole brain FC at rest during exposure to nature and urban soundscapes and to explore their relationship to behavior.
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Affiliation(s)
- Emil Stobbe
- Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Lentzeallee 94, 14195, Berlin, Germany.
| | - Caroline Garcia Forlim
- Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Lentzeallee 94, 14195, Berlin, Germany; University Medical Center Hamburg-Eppendorf, Department of Psychiatry and Psychotherapy, Martinistr. 52, 20251, Hamburg, Germany
| | - Simone Kühn
- Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Lentzeallee 94, 14195, Berlin, Germany; Max Planck-UCL Center for Computational Psychiatry and Ageing Research, Germany; University Medical Center Hamburg-Eppendorf, Department of Psychiatry and Psychotherapy, Martinistr. 52, 20251, Hamburg, Germany
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12
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Yildiz C, Medina I. Thermodynamic Analysis to Evaluate the Effect of Diet on Brain Glucose Metabolism: The Case of Fish Oil. Nutrients 2024; 16:631. [PMID: 38474759 DOI: 10.3390/nu16050631] [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: 01/16/2024] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Inefficient glucose metabolism and decreased ATP production in the brain are linked to ageing, cognitive decline, and neurodegenerative diseases (NDDs). This study employed thermodynamic analysis to assess the effect of fish oil supplementation on glucose metabolism in ageing brains. Data from previous studies on glucose metabolism in the aged human brain and grey mouse lemur brains were examined. The results demonstrated that Omega-3 fish oil supplementation in grey mouse lemurs increased entropy generation and decreased Gibbs free energy across all brain regions. Specifically, there was a 47.4% increase in entropy generation and a 47.4 decrease in Gibbs free energy in the whole brain, indicating improved metabolic efficiency. In the human model, looking at the specific brain regions, supplementation with Omega-3 polyunsaturated fatty acids (n-3 PUFAs) reduced the entropy generation difference between elderly and young individuals in the cerebellum and particular parts of the brain cortex, namely the anterior cingulate and occipital lobe, with 100%, 14.29%, and 20% reductions, respectively. The Gibbs free energy difference was reduced only in the anterior cingulate by 60.64%. This research underscores that the application of thermodynamics is a comparable and powerful tool in comprehending the dynamics and metabolic intricacies within the brain.
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Affiliation(s)
- Cennet Yildiz
- Marine Chemistry, Instituto de Investigaciones Marinas CSIC, 36208 Vigo, Spain
- Biothermodynamics, School of Life Sciences, Technische Universität München, 85354 Freising, Germany
| | - Isabel Medina
- Marine Chemistry, Instituto de Investigaciones Marinas CSIC, 36208 Vigo, Spain
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13
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Jia H, Wang L. Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model. Behav Sci (Basel) 2024; 14:54. [PMID: 38247706 PMCID: PMC10813203 DOI: 10.3390/bs14010054] [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: 11/16/2023] [Revised: 12/30/2023] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
This paper provides a systematic review of the transfer and quantification of the concept of entropy in multidisciplinary fields and delves into its future applications and research directions in organizational management psychology based on its core characteristics. We first comprehensively reviewed the conceptual evolution of entropy in disciplines such as physics, information theory, and psychology, revealing its complexity and diversity as an interdisciplinary concept. Subsequently, we analyzed the quantification methods of entropy in a multidisciplinary context and pointed out that their calculation methods have both specificity and commonality across different disciplines. Subsequently, the paper reviewed the research on how individuals cope with uncertainty in entropy increase, redefined psychological entropy from the perspective of organizational management psychology, and proposed an "entropy-based proactive control model" at the individual level. This model is built around the core connotation of entropy, covering four dimensions: learning orientation, goal orientation, change orientation, and risk taking. We believe that psychological entropy, as a meta structure of individuals, can simulate, explain, and predict the process of how individuals manage and control "entropy" in an organizational environment from a dynamic perspective. This understanding enables psychological entropy to integrate a series of positive psychological constructs (e.g., lean spirit), providing extensive predictive and explanatory power for various behaviors of individuals in organizations. This paper provides a new direction for the application of the concept of entropy in psychology, especially for theoretical development and practical application in the field of organizational management.
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Affiliation(s)
| | - Lei Wang
- School of Psychological and Cognitive Sciences and Beijing Key Lab for Behavior and Mental Health, Peking University, Beijing 100871, China;
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14
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Melnyk K, Friedman L, Komogortsev OV. What can entropy metrics tell us about the characteristics of ocular fixation trajectories? PLoS One 2024; 19:e0291823. [PMID: 38166054 PMCID: PMC10760742 DOI: 10.1371/journal.pone.0291823] [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: 12/10/2022] [Accepted: 09/06/2023] [Indexed: 01/04/2024] Open
Abstract
In this study, we provide a detailed analysis of entropy measures calculated for fixation eye movement trajectories from the three different datasets. We employed six key metrics (Fuzzy, Increment, Sample, Gridded Distribution, Phase, and Spectral Entropies). We calculate these six metrics on three sets of fixations: (1) fixations from the GazeCom dataset, (2) fixations from what we refer to as the "Lund" dataset, and (3) fixations from our own research laboratory ("OK Lab" dataset). For each entropy measure, for each dataset, we closely examined the 36 fixations with the highest entropy and the 36 fixations with the lowest entropy. From this, it was clear that the nature of the information from our entropy metrics depended on which dataset was evaluated. These entropy metrics found various types of misclassified fixations in the GazeCom dataset. Two entropy metrics also detected fixation with substantial linear drift. For the Lund dataset, the only finding was that low spectral entropy was associated with what we call "bumpy" fixations. These are fixations with low-frequency oscillations. For the OK Lab dataset, three entropies found fixations with high-frequency noise which probably represent ocular microtremor. In this dataset, one entropy found fixations with linear drift. The between-dataset results are discussed in terms of the number of fixations in each dataset, the different eye movement stimuli employed, and the method of eye movement classification.
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Affiliation(s)
- Kateryna Melnyk
- Department of Computer Science, Texas State University, San Marcos, TX, United States of America
| | - Lee Friedman
- Department of Computer Science, Texas State University, San Marcos, TX, United States of America
| | - Oleg V. Komogortsev
- Department of Computer Science, Texas State University, San Marcos, TX, United States of America
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15
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Lin C, Huang C, Chang W, Chang Y, Liu H, Ng S, Lin H, Lee TM, Lee S, Wu S. Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI. Brain Behav 2024; 14:e3348. [PMID: 38376042 PMCID: PMC10790060 DOI: 10.1002/brb3.3348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.
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Affiliation(s)
- Chemin Lin
- Department of PsychiatryKeelung Chang Gung Memorial HospitalKeelungTaiwan
- College of MedicineChang Gung UniversityTaoyuanTaiwan
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
| | - Chih‐Mao Huang
- Department of Biological Science and TechnologyNational Yang Ming Chiao Tung UniversityHsinchuTaiwan
| | - Wei Chang
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
| | - You‐Xun Chang
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
| | - Ho‐Ling Liu
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
- Department of Imaging PhysicsUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Shu‐Hang Ng
- Department of Head and Neck Oncology GroupLinkou Chang Gung Memorial Hospital and Chang Gung UniversityTaoyuanTaiwan
- Department of Diagnostic RadiologyLinkou Chang Gung Memorial Hospital and Chang Gung UniversityTaoyuanTaiwan
| | - Huang‐Li Lin
- Department of PsychiatryLinkou Chang Gung Memorial HospitalTaoyuanTaiwan
| | - Tatia Mei‐Chun Lee
- Community Medicine Research CenterChang Gung Memorial HospitalKeelungTaiwan
- Laboratory of Neuropsychology and Human NeuroscienceThe University of Hong KongPok Fu LamHong Kong
- State Key Laboratory of Brain and Cognitive ScienceThe University of Hong KongPok Fu LamHong Kong
| | - Shwu‐Hua Lee
- Department of PsychiatryLinkou Chang Gung Memorial HospitalTaoyuanTaiwan
| | - Shun‐Chi Wu
- Department of Engineering and System ScienceNational Tsing Hua UniversityHsinchuTaiwan
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16
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Guzmán-Vargas L, Zabaleta-Ortega A, Guzmán-Sáenz A. Simplicial complex entropy for time series analysis. Sci Rep 2023; 13:22696. [PMID: 38123652 PMCID: PMC10733285 DOI: 10.1038/s41598-023-49958-6] [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: 10/13/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
The complex behavior of many systems in nature requires the application of robust methodologies capable of identifying changes in their dynamics. In the case of time series (which are sensed values of a system during a time interval), several methods have been proposed to evaluate their irregularity. However, for some types of dynamics such as stochastic and chaotic, new approaches are required that can provide a better characterization of them. In this paper we present the simplicial complex approximate entropy, which is based on the conditional probability of the occurrence of elements of a simplicial complex. Our results show that this entropy measure provides a wide range of values with details not easily identifiable with standard methods. In particular, we show that our method is able to quantify the irregularity in simulated random sequences and those from low-dimensional chaotic dynamics. Furthermore, it is possible to consistently differentiate cardiac interbeat sequences from healthy subjects and from patients with heart failure, as well as to identify changes between dynamical states of coupled chaotic maps. Our results highlight the importance of the structures revealed by the simplicial complexes, which holds promise for applications of this approach in various contexts.
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Affiliation(s)
- Lev Guzmán-Vargas
- Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, 07340, Mexico City, Mexico.
| | - Alvaro Zabaleta-Ortega
- Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, 07340, Mexico City, Mexico
| | - Aldo Guzmán-Sáenz
- Topological Data Analysis in Genomics, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
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17
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Singleton SP, Velidi P, Schilling L, Luppi AI, Jamison K, Parkes L, Kuceyeski A. Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568762. [PMID: 38077021 PMCID: PMC10705230 DOI: 10.1101/2023.11.27.568762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Heavy alcohol use and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of AUD has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. We find that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of AUD.
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Affiliation(s)
- S Parker Singleton
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Puneet Velidi
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, U.S.A
| | - Louisa Schilling
- Montreal Neurological Institute, McGill Univeristy, Montreal, CA
| | - Andrea I Luppi
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
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18
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Averna A, Coelli S, Ferrara R, Cerutti S, Priori A, Bianchi AM. Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease. J Neural Eng 2023; 20:051001. [PMID: 37746822 DOI: 10.1088/1741-2552/acf8fa] [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: 12/16/2022] [Accepted: 09/12/2023] [Indexed: 09/26/2023]
Abstract
Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.
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Affiliation(s)
- Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Rosanna Ferrara
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Sergio Cerutti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Alberto Priori
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Hipólito I, Mago J, Rosas FE, Carhart-Harris R. Pattern breaking: a complex systems approach to psychedelic medicine. Neurosci Conscious 2023; 2023:niad017. [PMID: 37424966 PMCID: PMC10325487 DOI: 10.1093/nc/niad017] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/19/2023] [Accepted: 06/08/2023] [Indexed: 07/11/2023] Open
Abstract
Recent research has demonstrated the potential of psychedelic therapy for mental health care. However, the psychological experience underlying its therapeutic effects remains poorly understood. This paper proposes a framework that suggests psychedelics act as destabilizers, both psychologically and neurophysiologically. Drawing on the 'entropic brain' hypothesis and the 'RElaxed Beliefs Under pSychedelics' model, this paper focuses on the richness of psychological experience. Through a complex systems theory perspective, we suggest that psychedelics destabilize fixed points or attractors, breaking reinforced patterns of thinking and behaving. Our approach explains how psychedelic-induced increases in brain entropy destabilize neurophysiological set points and lead to new conceptualizations of psychedelic psychotherapy. These insights have important implications for risk mitigation and treatment optimization in psychedelic medicine, both during the peak psychedelic experience and during the subacute period of potential recovery.
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Affiliation(s)
- Inês Hipólito
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin 10117, Germany
- Department of Philosophy, Macquarie University, New South Wales 2109, Australia
| | - Jonas Mago
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom
- Integrative Program in Neuroscience, McGill University, Montreal, Quebec QC H3A, Canada
| | - Fernando E Rosas
- Department of Brain Sciences, Centre for Psychedelic Research, Imperial College London, London SW7 2BX, United Kingdom
- Centre for Complexity Science, Imperial College London, London SW7 2BX, United Kingdom
- Data Science Institute, Imperial College London, London SW7 2BX, United Kingdom
- Department of Informatics, University of Sussex, Brighton BN1 9RH, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford OX3 9BX, United Kingdom
| | - Robin Carhart-Harris
- Department of Brain Sciences, Centre for Psychedelic Research, Imperial College London, London SW7 2BX, United Kingdom
- Psychedelics Division, University of California San Francisco, San Francisco, CA 92521, United States
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20
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Walter N, Meinersen-Schmidt N, Kulla P, Loew T, Kruse J, Hinterberger T. Sensory-Processing Sensitivity Is Associated with Increased Neural Entropy. ENTROPY (BASEL, SWITZERLAND) 2023; 25:890. [PMID: 37372234 DOI: 10.3390/e25060890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND This study aimed at answering the following research questions: (1) Does the self-reported level of sensory-processing sensitivity (SPS) correlate with complexity, or criticality features of the electroencephalogram (EEG)? (2) Are there significant EEG differences comparing individuals with high and low levels of SPS? METHODS One hundred fifteen participants were measured with 64-channel EEG during a task-free resting state. The data were analyzed using criticality theory tools (detrended fluctuation analysis, neuronal avalanche analysis) and complexity measures (sample entropy, Higuchi's fractal dimension). Correlations with the 'Highly Sensitive Person Scale' (HSPS-G) scores were determined. Then, the cohort's lowest and the highest 30% were contrasted as opposites. EEG features were compared between the two groups by applying a Wilcoxon signed-rank test. RESULTS During resting with eyes open, HSPS-G scores correlated significantly positively with the sample entropy and Higuchi's fractal dimension (Spearman's ρ = 0.22, p < 0.05). The highly sensitive group revealed higher sample entropy values (1.83 ± 0.10 vs. 1.77 ± 0.13, p = 0.031). The increased sample entropy in the highly sensitive group was most pronounced in the central, temporal, and parietal regions. CONCLUSION For the first time, neurophysiological complexity features associated with SPS during a task-free resting state were demonstrated. Evidence is provided that neural processes differ between low- and highly-sensitive persons, whereby the latter displayed increased neural entropy. The findings support the central theoretical assumption of enhanced information processing and could be important for developing biomarkers for clinical diagnostics.
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Affiliation(s)
- Nike Walter
- Department of Psychosomatic Medicine, University Hospital Regensburg, 93059 Regensburg, Germany
| | - Nicole Meinersen-Schmidt
- Department for Clinical Psychology and Trauma Therapy, University of the Bundeswehr Munich, 85579 Neubiberg, Germany
| | - Patricia Kulla
- Department for Clinical Psychology and Trauma Therapy, University of the Bundeswehr Munich, 85579 Neubiberg, Germany
| | - Thomas Loew
- Department of Psychosomatic Medicine, University Hospital Regensburg, 93059 Regensburg, Germany
| | - Joachim Kruse
- Department for Clinical Psychology and Trauma Therapy, University of the Bundeswehr Munich, 85579 Neubiberg, Germany
| | - Thilo Hinterberger
- Department of Psychosomatic Medicine, University Hospital Regensburg, 93059 Regensburg, Germany
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21
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Hull A, Morton JB. Activity-State Entropy: A Novel Brain Entropy Measure Based on Spatial Patterns of Activity. J Neurosci Methods 2023; 393:109868. [PMID: 37120138 DOI: 10.1016/j.jneumeth.2023.109868] [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: 11/10/2022] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND Brain entropy is a measure of the complexity of brain activity that has been linked to various cognitive abilities. The measure is based on Shannon Entropy, a measure from Information Theory that quantifies the information capacity of a system from the probability distribution of its states. Most fMRI studies measure brain entropy at the voxel level as time-series entropy and assume that entropic time-series indicate complex large-scale spatiotemporal patterns of activity. New Method We developed a novel measure of brain entropy called Activity-State Entropy. The method quantifies entropy based on underlying patterns of coactivation identified using Principal Components Analysis. These patterns, termed eigenactivity states, combine in time-varying proportions. RESULTS We showed that Activity-State Entropy is sensitive to the complexity of the spatiotemporal patterns of activity in simulated fMRI data. We then applied this measure to real resting-state fMRI data and found that the eigenactivity states that explained the most variance in the data were comprised of large clusters of coactivating voxels, including clusters within Default Mode Network regions. More entropic brains were increasingly influenced by eigenactivity states comprised of smaller and more sparsely distributed clusters. Comparison to Existing Methods We compared Activity-State Entropy to Sample Entropy and Dispersion Entropy, two time-series entropy measures commonly used in neuroimaging research, and found all three measures were positively correlated. CONCLUSIONS Activity-State Entropy provides a measure of the spatiotemporal complexity of brain activity that complements time-series based measures of brain entropy.
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Affiliation(s)
- Adam Hull
- Undergraduate Program in Neuroscience, Western University, London, Canada, N6A 3K7.
| | - J Bruce Morton
- Department of Psychology, Western University, London, Canada, N6A 3K7.
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22
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Perez Velazquez JL, Mateos DM, Guevara R. Is the tendency to maximise energy distribution an optimal collective activity for biological purposes? A proposal for a global principle of biological organization. Heliyon 2023; 9:e15005. [PMID: 37095928 PMCID: PMC10121639 DOI: 10.1016/j.heliyon.2023.e15005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 03/06/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
Our purpose is to address the biological problem of finding foundations of the organization in the collective activity among cell networks in the nervous system, at the meso/macroscale, giving rise to cognition and consciousness. But in doing so, we encounter another problem related to the interpretation of methods to assess the neural interactions and organization of the neurodynamics, because thermodynamic notions, which have precise meaning only under specific conditions, have been widely employed in these studies. The consequence is that apparently contradictory results appear in the literature, but these contradictions diminish upon the considerations of the specific circumstances of each experiment. After clarifying some of these controversial points and surveying some experimental results, we propose that a necessary condition for cognition/consciousness to emerge is to have available enough energy, or cellular activity; and a sufficient condition is the multiplicity of configurations in which cell networks can communicate, resulting in non-uniform energy distribution, the generation and dissipation of energy gradients due to the constant activity. The diversity of sensorimotor processing of higher animals needs a flexible, fluctuating web on neuronal connections, and we review results supporting such multiplicity of configurations among brain regions associated with conscious awareness and healthy brain states. These ideas may reveal possible fundamental principles of brain organization that could be extended to other natural phenomena and how healthy activity may derive to pathological states.
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23
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Fagerholm ED, Dezhina Z, Moran RJ, Turkheimer FE, Leech R. A primer on entropy in neuroscience. Neurosci Biobehav Rev 2023; 146:105070. [PMID: 36736445 DOI: 10.1016/j.neubiorev.2023.105070] [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: 09/29/2022] [Revised: 01/16/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023]
Abstract
Entropy is not just a property of a system - it is a property of a system and an observer. Specifically, entropy is a measure of the amount of hidden information in a system that arises due to an observer's limitations. Here we provide an account of entropy from first principles in statistical mechanics with the aid of toy models of neural systems. Specifically, we describe the distinction between micro and macrostates in the context of simplified binary-state neurons and the characteristics of entropy required to capture an associated measure of hidden information. We discuss the origin of the mathematical form of entropy via the indistinguishable re-arrangements of discrete-state neurons and show the way in which the arguments are extended into a phase space description for continuous large-scale neural systems. Finally, we show the ways in which limitations in neuroimaging resolution, as represented by coarse graining operations in phase space, lead to an increase in entropy in time as per the second law of thermodynamics. It is our hope that this primer will support the increasing number of studies that use entropy as a way of characterising neuroimaging timeseries and of making inferences about brain states.
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Affiliation(s)
- Erik D Fagerholm
- Department of Neuroimaging, King's College London, United Kingdom.
| | - Zalina Dezhina
- Department of Neuroimaging, King's College London, United Kingdom
| | - Rosalyn J Moran
- Department of Neuroimaging, King's College London, United Kingdom
| | | | - Robert Leech
- Department of Neuroimaging, King's College London, United Kingdom
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24
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Orłowski P, Bola M. Sensory modality defines the relation between EEG Lempel-Ziv diversity and meaningfulness of a stimulus. Sci Rep 2023; 13:3453. [PMID: 36859725 PMCID: PMC9977735 DOI: 10.1038/s41598-023-30639-3] [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: 06/27/2022] [Accepted: 02/27/2023] [Indexed: 03/03/2023] Open
Abstract
Diversity of brain activity is a robust neural correlate of global states of consciousness. It has been proposed that diversity measures specifically reflect the temporal variability of conscious experience. Previous studies supported this hypothesis by showing that perception of meaningful visual stimuli causes richer, more-variable experiences than perception of meaningless stimuli, and this is reflected in greater brain signal diversity. To investigate whether this relation is consistent across sensory modalities, to participants we presented three versions of naturalistic visual and auditory stimuli (videos and audiobooks) that varied in the amount of meaning (original, scrambled, and noise), while recording electroencephalographic signals. We report three main findings. First, greater meaningfulness of visual stimuli was related to higher Lempel-Ziv diversity of EEG signals, but the opposite effect was found in the auditory modality. Second, visual perception was related to generally higher EEG diversity than auditory perception. Third, perception of meaningful visual stimuli and auditory stimuli respectively resulted in higher and lower EEG diversity in comparison to the resting state. In conclusion, the signal diversity of continuous brain signals depends on the stimulated sensory modality, therefore it is not a generic index of the variability of conscious experience.
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Affiliation(s)
- Paweł Orłowski
- grid.419305.a0000 0001 1943 2944Laboratory of Brain Imaging, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warsaw, Poland
| | - Michał Bola
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland.
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25
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Zheng J, Li Y, Zhai Y, Zhang N, Yu H, Tang C, Yan Z, Luo E, Xie K. Effects of sampling rate on multiscale entropy of electroencephalogram time series. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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26
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El Youssef N, Jegou A, Makhalova J, Naccache L, Bénar C, Bartolomei F. Consciousness alteration in focal epilepsy is related to loss of signal complexity and information processing. Sci Rep 2022; 12:22276. [PMID: 36566285 PMCID: PMC9789957 DOI: 10.1038/s41598-022-25861-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 12/06/2022] [Indexed: 12/25/2022] Open
Abstract
Alteration of awareness is a main feature of focal epileptic seizures. In this work, we studied how the information contained in EEG signals was modified during temporal lobe seizures with altered awareness by using permutation entropy (PE) as a measure of the complexity of the signal. PE estimation was performed in thirty-six seizures of sixteen patients with temporal lobe epilepsy who underwent SEEG recordings. We tested whether altered awareness (based on the Consciousness Seizure Score) was correlated with a loss of signal complexity. We estimated global changes in PE as well as regional changes to gain insight into the mechanisms associated with awareness impairment. Our results reveal a positive correlation between the decrease of entropy and the consciousness score as well as the existence of a threshold on entropy that could discriminate seizures with no alteration of awareness from seizures with profound alteration of awareness. The loss of signal complexity was diffuse, extending bilaterally and to the associative cortices, in patients with profound alteration of awareness and limited to the temporal mesial structures in patients with no alteration of awareness. Thus PE is a promising tool to discriminate between the different subgroups of awareness alteration in TLE.
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Affiliation(s)
- Nada El Youssef
- grid.411266.60000 0001 0404 1115APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
| | - Aude Jegou
- grid.5399.60000 0001 2176 4817Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Makhalova
- grid.411266.60000 0001 0404 1115APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France ,grid.411266.60000 0001 0404 1115APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Lionel Naccache
- grid.50550.350000 0001 2175 4109APHP, Departments of Neurology & Clinical Neurophysiology Pitié Salpêtrière Hospital, Paris, France
| | - Christian Bénar
- grid.5399.60000 0001 2176 4817Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- grid.411266.60000 0001 0404 1115APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France ,grid.5399.60000 0001 2176 4817Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France ,grid.411266.60000 0001 0404 1115Service d’Epileptologie et de Rythmologie Cérébrale, Hôpital Timone, 264 Rue Saint-Pierre, 13005 Marseille, France
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27
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Pappalettera C, Cacciotti A, Nucci L, Miraglia F, Rossini PM, Vecchio F. Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain. GeroScience 2022; 45:1131-1145. [PMID: 36538178 PMCID: PMC9886767 DOI: 10.1007/s11357-022-00710-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/03/2022] [Indexed: 12/24/2022] Open
Abstract
Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-11 Hz), alpha 2 (11-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive-specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity.
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Affiliation(s)
- Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy. .,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
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28
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Neural complexity is a common denominator of human consciousness across diverse regimes of cortical dynamics. Commun Biol 2022; 5:1374. [PMID: 36522453 PMCID: PMC9755290 DOI: 10.1038/s42003-022-04331-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
What is the common denominator of consciousness across divergent regimes of cortical dynamics? Does consciousness show itself in decibels or in bits? To address these questions, we introduce a testbed for evaluating electroencephalogram (EEG) biomarkers of consciousness using dissociations between neural oscillations and consciousness caused by rare genetic disorders. Children with Angelman syndrome (AS) exhibit sleep-like neural dynamics during wakefulness. Conversely, children with duplication 15q11.2-13.1 syndrome (Dup15q) exhibit wake-like neural dynamics during non-rapid eye movement (NREM) sleep. To identify highly generalizable biomarkers of consciousness, we trained regularized logistic regression classifiers on EEG data from wakefulness and NREM sleep in children with AS using both entropy measures of neural complexity and spectral (i.e., neural oscillatory) EEG features. For each set of features, we then validated these classifiers using EEG from neurotypical (NT) children and abnormal EEGs from children with Dup15q. Our results show that the classification performance of entropy-based EEG biomarkers of conscious state is not upper-bounded by that of spectral EEG features, which are outperformed by entropy features. Entropy-based biomarkers of consciousness may thus be highly adaptable and should be investigated further in situations where spectral EEG features have shown limited success, such as detecting covert consciousness or anesthesia awareness.
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29
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Pelc K, Gajewska A, Napiórkowski N, Dan J, Verhoeven C, Dan B. Multiscale entropy as a metric of brain maturation in a large cohort of typically developing children born preterm using longitudinal high-density EEG in the first two years of life. Physiol Meas 2022; 43. [PMID: 36374000 DOI: 10.1088/1361-6579/aca26c] [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: 08/23/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022]
Abstract
Objective.We aimed to analyze whether complexity of brain electrical activity (EEG) measured by multiscale entropy (MSE) increases with brain maturation during the first two years of life. We also aimed to investigate whether this complexity shows regional differences across the brain, and whether changes in complexity are influenced by extrauterine life experience duration.Approach.We measured MSE of EEG signals recorded longitudinally using a high-density setup (64 or 128 electrodes) in 84 typically developing infants born preterm (<32 weeks' gestation) from term age to two years. We analyzed the complexity index and maximum value of MSE over increasing age, across brain regions, and in function of extrauterine life duration, and used correlation matrices as a metric of functional connectivity of the cerebral cortex.Main results.We found an increase of strong inter-channel correlation of MSE (R > 0.8) with increasing age. Regional analysis showed significantly increased MSE between 3 and 24 months of corrected age in the posterior and middle regions with respect to the anterior region. We found a weak relationship (adjusted R2= 0.135) between MSE and extrauterine life duration.Significance.These findings suggest that brain functional connectivity increases with maturation during the first two years of life. EEG complexity shows regional differences with earlier maturation of the visual cortex and brain regions involved in joint attention than of regions involved in cognitive analysis, abstract thought, and social behavior regulation. Finally, our MSE analysis suggested only a weak influence of early extrauterine life experiences (prior to term age) on EEG complexity.
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Affiliation(s)
- Karine Pelc
- Université libre de Bruxelles (ULB), Facuty of Motor Sciences, Brussels, Belgium.,Inkendaal Rehabilitation Hospital, Vlezenbeek, Belgium
| | | | | | - Jonathan Dan
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium.,Byteflies, Berchem, Belgium
| | - Caroline Verhoeven
- Université libre de Bruxelles (ULB), Facuty of Medicine, Department of Mathematics Education, Brussels, Belgium
| | - Bernard Dan
- Inkendaal Rehabilitation Hospital, Vlezenbeek, Belgium.,Université libre de Bruxelles (ULB), Faculty of Psychology and Education Sciences, Brussels, Belgium
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30
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Zhang S, Liu A, Zhou Z, Huang Z, Cheng J, Chen D, Zhong Q, Yu Q, Peng Z, Hong M. Clinical features and power spectral entropy of electroencephalography in Wilson's disease with dystonia. Brain Behav 2022; 12:e2791. [PMID: 36282481 PMCID: PMC9759124 DOI: 10.1002/brb3.2791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/28/2022] [Accepted: 10/02/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To study the clinical features and power spectral entropy (PSE) of electroencephalography signals in Wilson's disease (WD) patients with dystonia. METHODS Several scale evaluations were performed to assess the clinical features of WD patients. Demographic information and electroencephalography signals were obtained in all subjects. RESULTS 34 WD patients with dystonia were recruited in the case group and 24 patients without dystonia were recruited in the control group. 20 healthy individuals were included in the healthy control group. The mean body mass index (BMI) in the case group was significantly lower than that in the controls (p < .05). The case group had significantly higher SAS, SDS, and Bucco-Facial-Apraxia Assessment scores (p < .05). Total BADS scores in the case group were lower than those in the control group (p < .01). Note that 94.11% of the case group presented with dysarthria and 70.59% of them suffered from dysphagia. Dysphagia was mainly related to the oral preparatory stage and oral stage. Mean power spectral entropy (PSE) values in the case group were significantly different (p < .05) from those in the control group and the healthy group across the different tasks. CONCLUSIONS The patients with dystonia were usually accompanied with low BMI, anxiety, depression, apraxia, executive dysfunction, dysarthria and dysphagia. The cortical activities of the WD patients with dystonia seemed to be more chaotic during the eyes-closed and reading tasks but lower during the swallowing stages than those in the control group.
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Affiliation(s)
- Shaoru Zhang
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Aiqun Liu
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Zhihua Zhou
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Zheng Huang
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Jing Cheng
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Danping Chen
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Qizhi Zhong
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Qingyun Yu
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Zhongxing Peng
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Mingfan Hong
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
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31
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McGovern HT, De Foe A, Biddell H, Leptourgos P, Corlett P, Bandara K, Hutchinson BT. Learned uncertainty: The free energy principle in anxiety. Front Psychol 2022; 13:943785. [PMID: 36248528 PMCID: PMC9559819 DOI: 10.3389/fpsyg.2022.943785] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness. Our account is simple: anxiety can be formalized as learned uncertainty. A biological system, having had persistent uncertainty in its past, will expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity—which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms.
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Affiliation(s)
- H. T. McGovern
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Alexander De Foe
- School of Educational Psychology and Counselling, Monash University, Melbourne, VIC, Australia
| | - Hannah Biddell
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Pantelis Leptourgos
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Philip Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Kavindu Bandara
- School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Brendan T. Hutchinson
- Research School of Psychology, The Australian National University, Canberra, ACT, Australia
- *Correspondence: Brendan T. Hutchinson,
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32
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Weninger L, Srivastava P, Zhou D, Kim JZ, Cornblath EJ, Bertolero MA, Habel U, Merhof D, Bassett DS. Information content of brain states is explained by structural constraints on state energetics. Phys Rev E 2022; 106:014401. [PMID: 35974521 DOI: 10.1103/physreve.106.014401] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/27/2022] [Indexed: 06/15/2023]
Abstract
Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. Being the physical substrate upon which information propagates, the structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in functional magnetic resonance imaging (fMRI) data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on cognitive context; its absolute level and spatial distribution depend on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions-especially those to high information content states-are less costly than expected from random network null models, thereby indicating the brains marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations.
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Affiliation(s)
- Leon Weninger
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Institute of Imaging & Computer Vision, RWTH Aachen University, 52072 Aachen, Germany
| | - Pragya Srivastava
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Dale Zhou
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jason Z Kim
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Eli J Cornblath
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Maxwell A Bertolero
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 10, Research Centre Jülich, 52428 Jülich, Germany
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, 52072 Aachen, Germany
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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33
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Liu Z, Si L, Xu W, Zhang K, Wang Q, Chen B, Wang G. Characteristics of EEG Microstate Sequences During Propofol-Induced Alterations of Brain Consciousness States. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1631-1641. [PMID: 35696466 DOI: 10.1109/tnsre.2022.3182705] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Monitoring the consciousness states of patients and ensuring the appropriate depth of anesthesia (DOA) is critical for the safe implementation of surgery. In this study, a high-density electroencephalogram (EEG) combined with blood drug concentration and behavioral response indicators was used to monitor propofol-induced sedation and evaluate the alterations in consciousness states. Microstate analysis, which can reflect the semi-stable state of the sub-second activation of the brain functional network, can be used to assess the brain's consciousness states. In this research, the EEG microstate sequences were constructed to compare the characteristics of corresponding sequences. Compared with the baseline (BS) state, the microstate sequences in the moderate sedation (MD) state exhibited higher complexity indexes of the multiscale sample entropy. With respect to the transition probability (TP) of microstates, most microstates tended to be converted into microstate C in the BS state. In contrast, they tended to be converted into microstate F in the MD state. The significant difference between the expected TP and observed TP could lead to the conclusion that hidden layers were present when there were changes in the consciousness states. According to the hidden Markov model, the accuracy of distinguishing the BS and MD states was 80.16%. The characteristics of microstate sequence revealed the variations in the brain states caused by alterations in consciousness states during anesthesia from a new perspective and presented a new idea for monitoring the DOA.
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34
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Huang H, Su Y, Niu Z, Liu G, Li X, Jiang M. Comatose Patients After Cardiopulmonary Resuscitation: An Analysis Based on Quantitative Methods of EEG Reactivity. Front Neurol 2022; 13:877406. [PMID: 35720067 PMCID: PMC9205205 DOI: 10.3389/fneur.2022.877406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Every year, approximately 50–110/1,00,000 people worldwide suffer from cardiac arrest, followed by hypoxic-ischemic encephalopathy after cardiopulmonary resuscitation (CPR), and approximately 40–66% of patients do not recover. The purpose of this study was to identify the brain network parameters and key brain regions associated with awakening by comparing the reactivity characteristics of the brain networks between the awakening and unawakening groups of CPR patients after coma, thereby providing a basis for further awakening interventions. Method This study involved a prospective cohort study. Using a 64-electrode electroencephalography (EEG) wireless 64A system, EEG signals were recorded from 16 comatose patients after CPR in the acute phase (<1 month) from 2019 to 2020. MATLAB (2017b) was used to quantitatively analyze the reactivity (power spectrum and entropy) and brain network characteristics (coherence and phase lag index) after pain stimulation. The patients were divided into an awakening group and an unawakening group based on their ability to execute commands or engage in repeated and continuous purposeful behavior after 3 months. The above parameters were compared to determine whether there were differences between the two groups. Results (1) Power spectrum: the awakening group had higher gamma, beta and alpha spectral power after pain stimulation in the frontal and parietal lobes, and lower delta and theta spectral power in the bilateral temporal and occipital lobes than the unawakening group. (2) Entropy: after pain stimulation, the awakening group had higher entropy in the frontal and parietal lobes and lower entropy in the temporal occipital lobes than the unawakening group. (3) Connectivity: after pain stimulation, the awakening group had stronger gamma and beta connectivity in nearly the whole brain, but weaker theta and delta connectivity in some brain regions (e.g., the frontal-occipital lobe and parietal-occipital lobe) than the unawakening group. Conclusion After CPR, comatose patients were more likely to awaken if there was a higher stimulation of fast-frequency band spectral power, higher entropy, stronger whole-brain connectivity and better retention of frontal-parietal lobe function after pain stimulation.
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Affiliation(s)
- Huijin Huang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- *Correspondence: Yingying Su
| | - Zikang Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Beijing Normal University, Beijing, China
- Zikang Niu
| | - Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Gang Liu
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Beijing Normal University, Beijing, China
| | - Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture. ENTROPY 2022; 24:e24050631. [PMID: 35626516 PMCID: PMC9141633 DOI: 10.3390/e24050631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 01/27/2023]
Abstract
Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used.
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36
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Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy. GeroScience 2022; 44:1599-1607. [PMID: 35344121 DOI: 10.1007/s11357-022-00552-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/19/2022] [Indexed: 11/04/2022] Open
Abstract
The objective of the present study is to explore the brain resting state differences between Parkinson's disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson's disease, and set up personalized rehabilitation programs.
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37
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Vrijdag XCE, van Waart H, Pullon RM, Sames C, Mitchell SJ, Sleigh JW. EEG functional connectivity is sensitive for nitrogen narcosis at 608 kPa. Sci Rep 2022; 12:4880. [PMID: 35318392 PMCID: PMC8940999 DOI: 10.1038/s41598-022-08869-8] [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: 11/10/2021] [Accepted: 03/14/2022] [Indexed: 12/21/2022] Open
Abstract
Divers commonly breathe air, containing nitrogen. Nitrogen under hyperbaric conditions is a narcotic gas. In dives beyond a notional threshold of 30 m depth (405 kPa) this can cause cognitive impairment, culminating in accidents due to poor decision making. Helium is known to have no narcotic effect. This study explored potential approaches to developing an electroencephalogram (EEG) functional connectivity metric to measure narcosis produced by nitrogen at hyperbaric pressures. Twelve human participants (five female) breathed air and heliox (in random order) at 284 and 608 kPa while recording 32-channel EEG and psychometric function. The degree of spatial functional connectivity, estimated using mutual information, was summarized with global efficiency. Air-breathing at 608 kPa (experienced as mild narcosis) caused a 35% increase in global efficiency compared to surface air-breathing (mean increase = 0.17, 95% CI [0.09–0.25], p = 0.001). Air-breathing at 284 kPa trended in a similar direction. Functional connectivity was modestly associated with psychometric impairment (mixed-effects model r2 = 0.60, receiver-operating-characteristic area, 0.67 [0.51–0.84], p = 0.02). Heliox breathing did not cause a significant change in functional connectivity. In conclusion, functional connectivity increased during hyperbaric air-breathing in a dose-dependent manner, but not while heliox-breathing. This suggests sensitivity to nitrogen narcosis specifically.
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Affiliation(s)
- Xavier C E Vrijdag
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.
| | - Hanna van Waart
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand
| | - Rebecca M Pullon
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.,Department of Anaesthesia, Waikato Hospital, Hamilton, 3240, New Zealand
| | - Chris Sames
- Slark Hyperbaric Unit, Waitemata District Health Board, Auckland, 0610, New Zealand
| | - Simon J Mitchell
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.,Slark Hyperbaric Unit, Waitemata District Health Board, Auckland, 0610, New Zealand.,Department of Anaesthesia, Auckland City Hospital, Auckland, 1023, New Zealand
| | - Jamie W Sleigh
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.,Department of Anaesthesia, Waikato Hospital, Hamilton, 3240, New Zealand
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Entropy Metrics Correlating with Higher Residual Functioning in Patients with Chronic Disorders of Consciousness. Brain Sci 2022; 12:brainsci12030332. [PMID: 35326288 PMCID: PMC8946802 DOI: 10.3390/brainsci12030332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/14/2022] [Accepted: 02/26/2022] [Indexed: 11/24/2022] Open
Abstract
To test the ability of different entropy measures to classify patients with different conditions of chronic disorder of consciousness, we applied the Lempel–Ziv complexity, the amplitude coalition entropy (ACE), and the synchrony coalition entropy (SCE) to the EEG signals recorded in 32 patients, clinically evaluated using the coma recovery scale revised (CRS-R). All the entropy measures indicated that differences found in the theta and alpha bands can distinguish patients in a minimal consciousness state (MCS) with respect to those in a vegetative state/unresponsive wakefulness state (VS/UWS). These differences were significant comparing the entropy measure performed on the anterior region of the left hemisphere and midline region. The values of theta-alpha entropy positively correlated with those of the CRS-R scores. Among the entropy measures, ACE most often highlighted significant differences. The higher values found in MCS were for the less impaired patients, according to their CRS-R, suggest that the preservation of signal entropy on the anterior region of the dominant hemisphere correlates with better preservation of consciousness, even in chronic conditions.
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Functional connectivity inference from fMRI data using multivariate information measures. Neural Netw 2022; 146:85-97. [PMID: 34847461 DOI: 10.1016/j.neunet.2021.11.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/01/2021] [Accepted: 11/11/2021] [Indexed: 02/05/2023]
Abstract
Shannon's entropy or an extension of Shannon's entropy can be used to quantify information transmission between or among variables. Mutual information is the pair-wise information that captures nonlinear relationships between variables. It is more robust than linear correlation methods. Beyond mutual information, two generalizations are defined for multivariate distributions: interaction information or co-information and total correlation or multi-mutual information. In comparison to mutual information, interaction information and total correlation are underutilized and poorly studied in applied neuroscience research. Quantifying information flow between brain regions is not explicitly explained in neuroscience by interaction information and total correlation. This article aims to clarify the distinctions between the neuroscience concepts of mutual information, interaction information, and total correlation. Additionally, we proposed a novel method for determining the interaction information between three variables using total correlation and conditional mutual information. On the other hand, how to apply it properly in practical situations. We supplied both simulation experiments and real neural studies to estimate functional connectivity in the brain with the above three higher-order information-theoretic approaches. In order to capture redundancy information for multivariate variables, we discovered that interaction information and total correlation were both robust, and it could be able to capture both well-known and yet-to-be-discovered functional brain connections.
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40
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Çatal Y, Gomez-Pilar J, Northoff G. Intrinsic dynamics and topography of sensory input systems. Cereb Cortex 2022; 32:4592-4604. [PMID: 35094077 PMCID: PMC9614113 DOI: 10.1093/cercor/bhab504] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 02/01/2023] Open
Abstract
The brain is continuously bombarded by external stimuli, which are processed in different input systems. The intrinsic features of these sensory input systems remain yet unclear. Investigating topography and dynamics of input systems is the goal of our study in order to better understand the intrinsic features that shape their neural processing. Using a functional magnetic resonance imaging dataset, we measured neural topography and dynamics of the input systems during rest and task states. Neural dynamics were probed by scale-free activity, measured with the power-law exponent (PLE), as well as by order/disorder as measured with sample entropy (SampEn). Our main findings during both rest and task states are: 1) differences in neural dynamics (PLE, SampEn) between regions within each of the three sensory input systems 2) differences in topography and dynamics among the three input systems; 3) PLE and SampEn correlate and, as demonstrated in simulation, show non-linear relationship in the critical range of PLE; 4) scale-free activity during rest mediates the transition of SampEn from rest to task as probed in a mediation model. We conclude that the sensory input systems are characterized by their intrinsic topographic and dynamic organization which, through scale-free activity, modulates their input processing.
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Affiliation(s)
- Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, Ontario K1Z 7K4, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid 47011, Spain,Centro de Investigación Biomédica en Red—Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid 28029, Spain
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41
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Scheijbeler EP, van Nifterick AM, Stam CJ, Hillebrand A, Gouw AA, de Haan W. Network-level permutation entropy of resting-state MEG recordings: a novel biomarker for early-stage Alzheimer’s disease? Netw Neurosci 2021; 6:382-400. [PMID: 35733433 PMCID: PMC9208018 DOI: 10.1162/netn_a_00224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer’s disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5–93.3%]) slightly outperformed PE (76.9% [60.3–93.4%]) and relative theta power–based models (76.9% [60.4–93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies. Functional network disruption is a well-established finding in Alzheimer’s disease. Sensitive network-based biomarkers are however not available. We aimed to detect neuronal dysfunction at a predementia (mild cognitive impairment, MCI) stage of Alzheimer’s disease, by applying a network-level neural variability measure to magnetoencephalography data: the inverted joint permutation entropy (JPEinv). This measure integrates information on local signal variability/complexity and nonlinear coupling. We found significant differences in JPEinv between subjects with subjective cognitive decline and MCI, primarily in the theta band. The diagnostic ability of the JPEinv was reported to be similar to that of relative theta power, the most potent neurophysiological biomarker of predementia Alzheimer’s disease to date.
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Affiliation(s)
- Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, 1007 MB Amsterdam, The Netherlands
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42
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Entropy for the Brain and Applied Computation. ENTROPY 2021; 23:e23121639. [PMID: 34945945 PMCID: PMC8700201 DOI: 10.3390/e23121639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 11/18/2022]
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Stancin I, Cifrek M, Jovic A. A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems. SENSORS 2021; 21:s21113786. [PMID: 34070732 PMCID: PMC8198610 DOI: 10.3390/s21113786] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 01/05/2023]
Abstract
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.
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Revilla-Vallejo M, Poza J, Gomez-Pilar J, Hornero R, Tola-Arribas MÁ, Cano M, Gómez C. Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease. ENTROPY (BASEL, SWITZERLAND) 2021; 23:500. [PMID: 33922270 PMCID: PMC8146430 DOI: 10.3390/e23050500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/10/2021] [Accepted: 04/19/2021] [Indexed: 11/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.
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Affiliation(s)
- Marcos Revilla-Vallejo
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, 47011 Valladolid, Spain
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, 47011 Valladolid, Spain
| | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- Department of Neurology, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, 47012 Valladolid, Spain;
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
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Yu WY, Low I, Chen C, Fuh JL, Chen LF. Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer's Disease and Mild Cognitive Impairment. ENTROPY (BASEL, SWITZERLAND) 2021; 23:427. [PMID: 33916588 PMCID: PMC8066899 DOI: 10.3390/e23040427] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 12/22/2022]
Abstract
Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer's disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a "transitional change" between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.
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Grants
- AS-BD-108-2 Academia Sinica, Taiwan
- MOST 109-2314-B-010-027, 107-2221-E-010-013, 109-2811-E-010-503, 108-2321-B-075-001, 109-2314-B-075-052-MY2 Ministry of Science and Technology, Taiwan
- VGHUST 110-G1-5-1, 110-G1-5-2, 109-V1-5-1, 109-V1-5-2 Veterans General Hospitals-University System of Taiwan Joint Research Program
- V110C-057 Taipei Veterans General Hospital
- Brain Research Center, National Yang Ming Chiao Tung University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project Taiwan Ministry of Education
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Affiliation(s)
- Wei-Yang Yu
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
| | - Intan Low
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Chien Chen
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Li-Fen Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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Frohlich J, Toker D, Monti MM. Consciousness among delta waves: a paradox? Brain 2021; 144:2257-2277. [PMID: 33693596 DOI: 10.1093/brain/awab095] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 02/12/2021] [Accepted: 02/25/2021] [Indexed: 01/29/2023] Open
Abstract
A common observation in EEG research is that consciousness vanishes with the appearance of delta (1 - 4 Hz) waves, particularly when those waves are high amplitude. High amplitude delta oscillations are very frequently observed in states of diminished consciousness, including slow wave sleep, anaesthesia, generalised epileptic seizures, and disorders of consciousness such as coma and vegetative state. This strong correlation between loss of consciousness and high amplitude delta oscillations is thought to stem from the widespread cortical deactivation that occurs during the "down states" or troughs of these slow oscillations. Recently, however, many studies have reported the presence of prominent delta activity during conscious states, which casts doubt on the hypothesis that high amplitude delta oscillations are an indicator of unconsciousness. These studies include work in Angelman syndrome, epilepsy, behavioural responsiveness during propofol anaesthesia, postoperative delirium, and states of dissociation from the environment such as dreaming and powerful psychedelic states. The foregoing studies complement an older, yet largely unacknowledged, body of literature that has documented awake, conscious patients with high amplitude delta oscillations in clinical reports from Rett syndrome, Lennox-Gastaut syndrome, schizophrenia, mitochondrial diseases, hepatic encephalopathy, and nonconvulsive status epilepticus. At the same time, a largely parallel body of recent work has reported convincing evidence that the complexity or entropy of EEG and magnetoencephalogram or MEG signals strongly relates to an individual's level of consciousness. Having reviewed this literature, we discuss plausible mechanisms that would resolve the seeming contradiction between high amplitude delta oscillations and consciousness. We also consider implications concerning theories of consciousness, such as integrated information theory and the entropic brain hypothesis. Finally, we conclude that false inferences of unconscious states can be best avoided by examining measures of electrophysiological complexity in addition to spectral power.
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Affiliation(s)
- Joel Frohlich
- Department of Psychology, University of California Los Angeles, 3423 Franz Hall, Los Angeles, California 90095, USA
| | - Daniel Toker
- Department of Psychology, University of California Los Angeles, 3423 Franz Hall, Los Angeles, California 90095, USA
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, 3423 Franz Hall, Los Angeles, California 90095, USA.,Department of Neurosurgery, UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095, USA
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Keshmiri S. Conditional Entropy: A Potential Digital Marker for Stress. ENTROPY (BASEL, SWITZERLAND) 2021; 23:286. [PMID: 33652891 PMCID: PMC7996836 DOI: 10.3390/e23030286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.
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Affiliation(s)
- Soheil Keshmiri
- Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan
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48
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Waschke L, Kloosterman NA, Obleser J, Garrett DD. Behavior needs neural variability. Neuron 2021; 109:751-766. [PMID: 33596406 DOI: 10.1016/j.neuron.2021.01.023] [Citation(s) in RCA: 105] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/16/2020] [Accepted: 01/22/2021] [Indexed: 01/26/2023]
Abstract
Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.
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Affiliation(s)
- Leonhard Waschke
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany.
| | - Niels A Kloosterman
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, 23562 Lübeck, Germany; Center of Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Douglas D Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
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Perpetuini D, Chiarelli AM, Filippini C, Cardone D, Croce P, Rotunno L, Anzoletti N, Zito M, Zappasodi F, Merla A. Working Memory Decline in Alzheimer's Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1380. [PMID: 33279924 PMCID: PMC7762102 DOI: 10.3390/e22121380] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/30/2020] [Accepted: 12/03/2020] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey-Osterrieth complex figure and Raven's progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.
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Affiliation(s)
- David Perpetuini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Antonio Maria Chiarelli
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Chiara Filippini
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Daniela Cardone
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Pierpaolo Croce
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Ludovica Rotunno
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Nelson Anzoletti
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Michele Zito
- Department of Medicine and Science of Ageing, University G. D’Annunzio, Via Dei Vestini 31, 66100 Chieti, Italy; (L.R.); (N.A.); (M.Z.)
| | - Filippo Zappasodi
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
| | - Arcangelo Merla
- Institute for Advanced Biomedical Technologies, Department of Neuroscience and Imaging, University G. D’Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, 66100 Chieti, Italy; (A.M.C.); (C.F.); (D.C.); (P.C.); (F.Z.); (A.M.)
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Zhang T, Wang H, Chen J, He E. Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1248. [PMID: 33287016 PMCID: PMC7711805 DOI: 10.3390/e22111248] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 10/31/2020] [Accepted: 11/01/2020] [Indexed: 01/12/2023]
Abstract
Unfavorable driving states can cause a large number of vehicle crashes and are significant factors in leading to traffic accidents. Hence, the aim of this research is to design a robust system to detect unfavorable driving states based on sample entropy feature analysis and multiple classification algorithms. Multi-channel Electroencephalography (EEG) signals are recorded from 16 participants while performing two types of driving tasks. For the purpose of selecting optimal feature sets for classification, principal component analysis (PCA) is adopted for reducing dimensionality of feature sets. Multiple classification algorithms, namely, K nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and logistic regression (LR) are employed to improve the accuracy of unfavorable driving state detection. We use 10-fold cross-validation to assess the performance of the proposed systems. It is found that the proposed detection system, based on PCA features and the cubic SVM classification algorithm, shows robustness as it obtains the highest accuracy of 97.81%, sensitivity of 96.93%, specificity of 98.73% and precision of 98.75%. Experimental results show that the system we designed can effectively monitor unfavorable driving states.
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Affiliation(s)
- Tao Zhang
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (T.Z.); (J.C.)
- College of Applied Technology, Shenyang University, Shenyang 110044, China
| | - Hong Wang
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (T.Z.); (J.C.)
| | - Jichi Chen
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; (T.Z.); (J.C.)
| | - Enqiu He
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China
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