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Vallesi A, Porcaro C, Visalli A, Fasolato D, Rossato F, Bussè C, Cagnin A. Resting-state EEG spectral and fractal features in dementia with Lewy bodies with and without visual hallucinations. Clin Neurophysiol 2024; 168:43-51. [PMID: 39442361 DOI: 10.1016/j.clinph.2024.10.004] [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: 01/11/2024] [Revised: 09/06/2024] [Accepted: 10/09/2024] [Indexed: 10/25/2024]
Abstract
OBJECTIVE Complex visual hallucinations (VH) are a core feature of dementia with Lewy bodies (DLB), though they may not occur in all patients. Power spectral density (PSD) analysis of resting-state EEG (rs-EEG) shows associations between some frequency bands (e.g., theta), individual alpha frequency (IAF) and VH. However, new tools that improve early differential diagnosis and symptom-based stratification with higher sensitivity and specificity, even within the DLB population, are desirable. We aimed to assess differences in rs-EEG data between DLB patients with VH (DLB-VH+) and without VH (DLB-VH-), comparing innovative non-linear approaches with more traditional linear ones. METHODS We retrospectively analyzed rs-EEG recordings of DLB-VH+, DLB-VH-, Alzheimer's disease patients and age-matched healthy controls. EEG was analyzed using the nonlinear Higuchi's Fractal Dimension (FD) measure, and the results were compared with those of entropy and standard linear methods based on PSD and IAF. RESULTS Only the FD measure could discriminate between DLB-VH+ and DLB-VH-. CONCLUSIONS In conclusion, rs-EEG differences between DLB-VH+ and DLB-VH- are better characterized by FD analysis than by a more traditional power spectrum approach. SIGNIFICANCE This suggests that the presence of complex VH is associated with less complex brain dynamics at rest, as reflected by the FD measure.
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Affiliation(s)
- Antonino Vallesi
- Dipartimento di Neuroscienze, Università degli Studi di Padova, Italy; Padova Neuroscience Center, Università degli Studi di Padova, Italy.
| | - Camillo Porcaro
- Dipartimento di Neuroscienze, Università degli Studi di Padova, Italy; Padova Neuroscience Center, Università degli Studi di Padova, Italy; Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy; Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Antonino Visalli
- IRCCS San Camillo Hospital, Lido di Venezia, Venice, Italy; Dipartimento di Psicologia Generale, University of Padova, Italy
| | - Davide Fasolato
- Dipartimento di Neuroscienze, Università degli Studi di Padova, Italy
| | - Francesco Rossato
- Dipartimento di Neuroscienze, Università degli Studi di Padova, Italy
| | - Cinzia Bussè
- Dipartimento di Neuroscienze, Università degli Studi di Padova, Italy
| | - Annachiara Cagnin
- Dipartimento di Neuroscienze, Università degli Studi di Padova, Italy; Padova Neuroscience Center, Università degli Studi di Padova, Italy.
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Tatti E, Cinti A, Serbina A, Luciani A, D'Urso G, Cacciola A, Quartarone A, Ghilardi MF. Resting-State EEG Alterations of Practice-Related Spectral Activity and Connectivity Patterns in Depression. Biomedicines 2024; 12:2054. [PMID: 39335567 PMCID: PMC11428598 DOI: 10.3390/biomedicines12092054] [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: 07/25/2024] [Revised: 08/13/2024] [Accepted: 09/05/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice in learning tasks increases power in beta range (13-30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after a learning task. METHODS We used publicly available resting-state EEG recordings (64 electrodes) from 122 subjects. Based on Beck Depression Inventory (BDI) scores, they were assigned to either a high BDI (hBDI, BDI > 13, N = 46) or a control (CTL, BDI < 7, N = 75) group. We analyzed spectral activity, theta-beta, and theta-gamma phase-amplitude coupling (PAC) of EEG recorded at rest before and after a learning task. RESULTS At baseline, compared to CTL, hBDI exhibited greater power in beta over fronto-parietal regions and in gamma over the right parieto-occipital area. At post task, power increased in all frequency ranges only in CTL. Theta-beta and theta-gamma PAC were greater in hBDI at baseline but not after the task. CONCLUSIONS The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon.
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Affiliation(s)
- Elisa Tatti
- Department of Molecular, Cellular & Biomedical Sciences, School of Medicine, City University of New York, New York, NY 10031, USA
| | - Alessandra Cinti
- Department of Molecular, Cellular & Biomedical Sciences, School of Medicine, City University of New York, New York, NY 10031, USA
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Unit of Neurology & Clinical Neurophysiology, Department of Medicine, Surgery & Neuroscience, University of Siena, 53100 Siena, Italy
| | - Anna Serbina
- Department of Molecular, Cellular & Biomedical Sciences, School of Medicine, City University of New York, New York, NY 10031, USA
- Department of Psychology, City College of New York, City University of New York, New York, NY 10031, USA
| | - Adalgisa Luciani
- Department of Molecular, Cellular & Biomedical Sciences, School of Medicine, City University of New York, New York, NY 10031, USA
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Giordano D'Urso
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences & Morphological and Functional Imaging, University of Messina, 98125 Messina, Italy
| | | | - Maria Felice Ghilardi
- Department of Molecular, Cellular & Biomedical Sciences, School of Medicine, City University of New York, New York, NY 10031, USA
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Javaid H, Nouman M, Cheaha D, Kumarnsit E, Chatpun S. Complexity measures reveal age-dependent changes in electroencephalogram during working memory task. Behav Brain Res 2024; 470:115070. [PMID: 38806100 DOI: 10.1016/j.bbr.2024.115070] [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: 12/11/2023] [Revised: 05/09/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.
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Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, Ex4 4QG, United Kingdom
| | - Muhammad Nouman
- Sirindhorn School of Prosthetics and Orthotics, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Dania Cheaha
- Biology program, Division of Biological Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand
| | - Ekkasit Kumarnsit
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
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Hernandez H, Baez S, Medel V, Moguilner S, Cuadros J, Santamaria-Garcia H, Tagliazucchi E, Valdes-Sosa PA, Lopera F, OchoaGómez JF, González-Hernández A, Bonilla-Santos J, Gonzalez-Montealegre RA, Aktürk T, Yıldırım E, Anghinah R, Legaz A, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, García AM, Huepe D, Caterina GD, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel C, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark R, Herzog R, Yerlikaya D, Güntekin B, Parra MA, Prado P, Ibanez A. Brain health in diverse settings: How age, demographics and cognition shape brain function. Neuroimage 2024; 295:120636. [PMID: 38777219 DOI: 10.1016/j.neuroimage.2024.120636] [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: 02/08/2024] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.
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Affiliation(s)
- Hernan Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sandra Baez
- Universidad de los Andes, Bogota, Colombia; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland
| | - Vicente Medel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Harvard Medical School, Boston, MA, USA
| | - Jhosmary Cuadros
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile; Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Hernando Santamaria-Garcia
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia; Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; University of Buenos Aires, Argentina
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia
| | | | | | | | | | - Tuba Aktürk
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Ebru Yıldırım
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil; Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil
| | - Agustina Legaz
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Görsev G Yener
- Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey; Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey; Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Alberto A Fernández Lucas
- Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center, Universidad de San Andréss, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez
| | - Gaetano Di Caterina
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
| | | | - Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | | | - Carlos Coronel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ruaridh Clark
- Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
| | - Ruben Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey; Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| | - Mario A Parra
- Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Trinity College Dublin, The University of Dublin, Dublin, Ireland.
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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [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: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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Porcaro C, Diciotti S, Madan CR, Marzi C. Editorial: Methods and application in fractal analysis of neuroimaging data. Front Hum Neurosci 2024; 18:1453284. [PMID: 39050380 PMCID: PMC11266171 DOI: 10.3389/fnhum.2024.1453284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies—National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi” – DEI, University of Bologna, Cesena, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | | | - Chiara Marzi
- Department of Statistics, Computer Science and Applications “Giuseppe Parenti,” University of Florence, Florence, Italy
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Porcaro C, Seppi D, Pellegrino G, Dainese F, Kassabian B, Pellegrino L, De Nardi G, Grego A, Corbetta M, Ferreri F. Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension. Front Neurosci 2024; 18:1401068. [PMID: 38911599 PMCID: PMC11192015 DOI: 10.3389/fnins.2024.1401068] [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: 03/14/2024] [Accepted: 05/20/2024] [Indexed: 06/25/2024] Open
Abstract
Objectives An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods. Materials Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC). Methods EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups. Results The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ. Discussion FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes. Conclusion Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Institute of Cognitive Sciences and Technologies (ISTC) – National Research Council (CNR), Rome, Italy
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Dario Seppi
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Giovanni Pellegrino
- Epilepsy Program, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Filippo Dainese
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Benedetta Kassabian
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Luciano Pellegrino
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Gianluigi De Nardi
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Alberto Grego
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Veneto Institute of Molecular Medicine (VIMM), Fondazione Biomedica, Padua, Italy
| | - Florinda Ferreri
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
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Zheng H, Xiong X, Zhang X. Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia. Brain Sci 2024; 14:565. [PMID: 38928565 PMCID: PMC11202180 DOI: 10.3390/brainsci14060565] [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: 05/15/2024] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.
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Affiliation(s)
- Huang Zheng
- School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China
| | - Xingliang Xiong
- Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China;
| | - Xuejun Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China
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9
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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10
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Fiorenzato E, Moaveninejad S, Weis L, Biundo R, Antonini A, Porcaro C. Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease. Mov Disord 2024; 39:305-317. [PMID: 38054573 DOI: 10.1002/mds.29678] [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: 07/26/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline. OBJECTIVES The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features. METHODS Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models. RESULTS FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity. CONCLUSIONS Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Eleonora Fiorenzato
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Sadaf Moaveninejad
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Luca Weis
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- IRCCS, San Camillo Hospital, Venice, Italy
| | - Roberta Biundo
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Angelo Antonini
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
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11
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Moaveninejad S, D'Onofrio V, Tecchio F, Ferracuti F, Iarlori S, Monteriù A, Porcaro C. Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107944. [PMID: 38064955 DOI: 10.1016/j.cmpb.2023.107944] [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: 09/06/2023] [Revised: 10/31/2023] [Accepted: 11/24/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVE The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. METHODS In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). RESULTS Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. CONCLUSIONS These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.
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Affiliation(s)
| | | | - Franca Tecchio
- Institute of Cognitive Sciences and Technologies (ISCT) - National Research Council (CNR), 00185 Rome, Italy
| | - Francesco Ferracuti
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Sabrina Iarlori
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Andrea Monteriù
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Camillo Porcaro
- Department of Neuroscience, University of Padova, 35128 Padua, Italy; Padova Neuroscience Center (PNC), University of Padova, 35131 Padua, Italy; Institute of Cognitive Sciences and Technologies (ISCT) - National Research Council (CNR), 00185 Rome, Italy; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK.
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12
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Pascarella A, Bruni V, Armonaite K, Porcaro C, Conti L, Cecconi F, Paulon L, Vitulano D, Tecchio F. Functional balance at rest of hemispheric homologs assessed via normalized compression distance. Front Neurosci 2024; 17:1261701. [PMID: 38333603 PMCID: PMC10851083 DOI: 10.3389/fnins.2023.1261701] [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: 07/19/2023] [Accepted: 12/13/2023] [Indexed: 02/10/2024] Open
Abstract
Introduction The formation and functioning of neural networks hinge critically on the balance between structurally homologous areas in the hemispheres. This balance, reflecting their physiological relationship, is fundamental for learning processes. In our study, we explore this functional homology in the resting state, employing a complexity measure that accounts for the temporal patterns in neurodynamics. Methods We used Normalized Compression Distance (NCD) to assess the similarity over time, neurodynamics, of the somatosensory areas associated with hand perception (S1). This assessment was conducted using magnetoencephalography (MEG) in conjunction with Functional Source Separation (FSS). Our primary hypothesis posited that neurodynamic similarity would be more pronounced within individual subjects than across different individuals. Additionally, we investigated whether this similarity is influenced by hemisphere or age at a population level. Results Our findings validate the hypothesis, indicating that NCD is a robust tool for capturing balanced functional homology between hemispheric regions. Notably, we observed a higher degree of neurodynamic similarity in the population within the left hemisphere compared to the right. Also, we found that intra-subject functional homology displayed greater variability in older individuals than in younger ones. Discussion Our approach could be instrumental in investigating chronic neurological conditions marked by imbalances in brain activity, such as depression, addiction, fatigue, and epilepsy. It holds potential for aiding in the development of new therapeutic strategies tailored to these complex conditions, though further research is needed to fully realize this potential.
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Affiliation(s)
- Annalisa Pascarella
- Istituto per le Applicazioni del Calcolo ‘Mauro Picone’, National Research Council of Italy, Rome, Italy
| | - Vittoria Bruni
- Istituto per le Applicazioni del Calcolo ‘Mauro Picone’, National Research Council of Italy, Rome, Italy
- Department of Basic and Applied Science for Engineering (SBAI), University of Rome ‘Sapienza’, Rome, Italy
| | | | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Laboratory of Electrophysiology for Translational neuroScience and Laboratory for Agent Based Social Simulation, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Rome, Italy
| | - Federico Cecconi
- Laboratory of Electrophysiology for Translational neuroScience and Laboratory for Agent Based Social Simulation, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational neuroScience and Laboratory for Agent Based Social Simulation, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy
- Luca Paulon, Independent Researcher, Rome, Italy
| | - Domenico Vitulano
- Department of Basic and Applied Science for Engineering (SBAI), University of Rome ‘Sapienza’, Rome, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational neuroScience and Laboratory for Agent Based Social Simulation, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy
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13
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Pirici D, Mogoanta L, Ion DA, Kumar-Singh S. Fractal Analysis in Neurodegenerative Diseases. ADVANCES IN NEUROBIOLOGY 2024; 36:365-384. [PMID: 38468042 DOI: 10.1007/978-3-031-47606-8_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Neurodegenerative diseases are defined by progressive nervous system dysfunction and death of neurons. The abnormal conformation and assembly of proteins is suggested to be the most probable cause for many of these neurodegenerative disorders, leading to the accumulation of abnormally aggregated proteins, for example, amyloid β (Aβ) (Alzheimer's disease and vascular dementia), tau protein (Alzheimer's disease and frontotemporal lobar degeneration), α-synuclein (Parkinson's disease and Lewy body dementia), polyglutamine expansion diseases (Huntington disease), or prion proteins (Creutzfeldt-Jakob disease). An aberrant gain-of-function mechanism toward excessive intraparenchymal accumulation thus represents a common pathogenic denominator in all these proteinopathies. Moreover, depending upon the predominant brain area involvement, these different neurodegenerative diseases lead to either movement disorders or dementia syndromes, although the underlying mechanism(s) can sometimes be very similar, and on other occasions, clinically similar syndromes can have quite distinct pathologies. Non-Euclidean image analysis approaches such as fractal dimension (FD) analysis have been applied extensively in quantifying highly variable morphopathological patterns, as well as many other connected biological processes; however, their application to understand and link abnormal proteinaceous depositions to other clinical and pathological features composing these syndromes is yet to be clarified. Thus, this short review aims to present the most important applications of FD in investigating the clinical-pathological spectrum of neurodegenerative diseases.
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Affiliation(s)
- Daniel Pirici
- Department of Histology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Laurentiu Mogoanta
- Department of Histology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Daniela Adriana Ion
- Department of Physiopathology, University of Medicine and Pharmacy Carol Davila, Bucharest, Romania
| | - Samir Kumar-Singh
- Molecular Pathology Group, Faculty of Medicine and Health Sciences, Cell Biology & Histology and Translational Neuroscience Department, University of Antwerp, Antwerpen, Belgium
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14
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Čukić M, Olejarzcyk E, Bachmann M. Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far? ADVANCES IN NEUROBIOLOGY 2024; 36:677-692. [PMID: 38468058 DOI: 10.1007/978-3-031-47606-8_34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Depression is currently one of the most complicated public health problems with the rising number of patients, increasing partly due to pandemics, but also due to increased existential insecurities and complicated aetiology of disease. Besides the tsunami of mental health issues, there are limitations imposed by ambiguous clinical rules of assessment of the symptoms and obsolete and inefficient standard therapy approaches. Here we are summarizing the neuroimaging results pointing out the actual complexity of the disease and novel attempts to detect depression that are evidence-based, mostly related to electrophysiology. It is repeatedly shown that the complexity of resting-state EEG recorded in patients suffering from depression is increased compared to healthy controls. We are discussing here how that can be interpreted and what we can learn about future effective therapies. Also, there is evidence that novel options of treatment, like different modalities of electromagnetic stimulation, are successful just because they are capable of decreasing that aberrated complexity. And complexity measures extracted from electrophysiological signals of depression patients can serve as excellent features for further machine learning models in order to automatize detection. In addition, after initial detection and even selection of responders for further therapy route, it is possible to monitor the therapeutic flow for one person, which leads us to possible tailored treatment for patients suffering from depression.
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Affiliation(s)
- Milena Čukić
- Empa Swiss Federal Labs for Materials Science and Technology, St. Gallen, Switzerland.
| | - Elzbieta Olejarzcyk
- Nalez Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
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15
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Porcaro C, Moaveninejad S, D'Onofrio V, DiIeva A. Fractal Time Series: Background, Estimation Methods, and Performances. ADVANCES IN NEUROBIOLOGY 2024; 36:95-137. [PMID: 38468029 DOI: 10.1007/978-3-031-47606-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience (DNS) and Padova Neuroscience Center (PNC), University of Padova, Padua, Italy.
- Institute of Cognitive Sciences and Technologies (ISTC) National Research Council (CNR), Rome, Italy.
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
| | | | | | - Antonio DiIeva
- Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia
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16
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Armonaite K, Conti L, Tecchio F. Fractal Neurodynamics. ADVANCES IN NEUROBIOLOGY 2024; 36:659-675. [PMID: 38468057 DOI: 10.1007/978-3-031-47606-8_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The neuronal ongoing electrical activity in the brain network, the neurodynamics, reflects the structure and functionality of generating neuronal pools. The activity of neurons due to their excitatory and inhibitory projections is associated with specific brain functions. Here, the purpose was to investigate if the local ongoing electrical activity exhibits its characteristic spectral and fractal features in wakefulness and sleep across and within subjects. Moreover, we aimed to show that measures typical of complex systems catch physiological features missed by linear spectral analyses. For this study, we concentrated on the evaluation of the power spectral density (PSD) and Higuchi fractal dimension (HFD) measures. Relevant clinical impact of the specific features of neurodynamics identification stands primarily in the potential of classifying cortical parcels according to their neurodynamics as well as enhancing the effectiveness of neuromodulation interventions to cure symptoms secondary to neuronal activity unbalances.
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Affiliation(s)
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Rome, Italy
| | - Franca Tecchio
- Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, Rome, Italy.
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17
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Olejarczyk E, Cukic M, Porcaro C, Zappasodi F, Tecchio F. Clinical Sensitivity of Fractal Neurodynamics. ADVANCES IN NEUROBIOLOGY 2024; 36:285-312. [PMID: 38468039 DOI: 10.1007/978-3-031-47606-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.
| | - Milena Cukic
- Department of Biomimetic Membranes and Textiles, EMPA Material Science and Technology, St. Gallen, Switzerland
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Filippo Zappasodi
- Department of Neuroscienze, Imaging and Clinical Sciences, Gabriele D'annunzio University, Chieti, Italy
| | - Franca Tecchio
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
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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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19
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Davoudi S, Schwartz T, Labbe A, Trainor L, Lippé S. Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants. Cereb Cortex 2023; 33:8734-8747. [PMID: 37143183 PMCID: PMC10321121 DOI: 10.1093/cercor/bhad154] [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: 11/15/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023] Open
Abstract
Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.
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Affiliation(s)
- Saeideh Davoudi
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Neuroscience, Université de Montréal, Montréal H3T 1J4, Canada
| | - Tyler Schwartz
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Aurélie Labbe
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Laurel Trainor
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton L8S 4K1, Canada
| | - Sarah Lippé
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Psychology, Université de Montréal, Montréal H2V 2S9, Canada
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20
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Milena Č, Romano C, De Tommasi F, Carassiti M, Formica D, Schena E, Massaroni C. Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031615. [PMID: 36772656 PMCID: PMC9920051 DOI: 10.3390/s23031615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/02/2023] [Accepted: 01/28/2023] [Indexed: 05/26/2023]
Abstract
Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart's electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body's surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.
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Affiliation(s)
- Čukić Milena
- Empa Materials Science and Technology, Biomimetic Membranes and Textiles, 9014 St. Gallen, Switzerland
- 3EGA B.V., 1062 KS Amsterdam, The Netherlands
| | - Chiara Romano
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Francesca De Tommasi
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
- Unit of Anesthesia, Intensive Care and Pain Management, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Massimiliano Carassiti
- Unit of Anesthesia, Intensive Care and Pain Management, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Domenico Formica
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 00128 Rome, Italy
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21
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Porcaro C, Avanaki K, Arias-Carrion O, Mørup M. Editorial: Combined EEG in research and diagnostics: Novel perspectives and improvements. Front Neurosci 2023; 17:1152394. [PMID: 36875646 PMCID: PMC9978703 DOI: 10.3389/fnins.2023.1152394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy.,Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Kamran Avanaki
- University of Illinois at Chicago, Chicago, IL, United States
| | - Oscar Arias-Carrion
- Unidad de Trastornos del Movimiento y Sueño, Hospital General Dr. Manuel Gea González, Mexico City, Mexico
| | - Morten Mørup
- Technical University of Denmark, Lyngby, Denmark
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22
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Elgammal YM, Zahran MA, Abdelsalam MM. A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm. Sci Rep 2022; 12:22381. [PMID: 36572791 PMCID: PMC9792538 DOI: 10.1038/s41598-022-26958-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 12/22/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's Disease (AD) is considered one of the most diseases that much prevalent among elderly people all over the world. AD is an incurable neurodegenerative disease affecting cognitive functions and were characterized by progressive and collective functions deteriorating. Remarkably, early detection of AD is essential for the development of new and invented treatment strategies. As Dementia causes irreversible damage to the brain neurons and leads to changes in its structure that can be described adequately within the framework of multifractals. Hence, the present work focus on developing a promising and efficient computing technique to pre-process and classify the AD disease especially in the early stages using multifractal geometry to extract the most changeable features due to AD. Then, A machine learning classification algorithm (K-Nearest Neighbor) has been implemented in order to classify and detect the main four early stages of AD. Two datasets have been used to ensure the validation of the proposed methodology. The proposed technique has achieved 99.4% accuracy and 100% sensitivity. The comparative results show that the proposed classification technique outperforms is recent techniques in terms of performance measures.
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Affiliation(s)
- Yasmina M Elgammal
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - M A Zahran
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Mohamed M Abdelsalam
- Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
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23
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Armonaite K, Conti L, Tecchio F. Book review: The fractal geometry of the brain. Front Neurosci 2022. [DOI: 10.3389/fnins.2022.1078376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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24
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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25
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Functional Source Separation-Identified Epileptic Network: Analysis Pipeline. Brain Sci 2022; 12:brainsci12091179. [PMID: 36138915 PMCID: PMC9496980 DOI: 10.3390/brainsci12091179] [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: 07/06/2022] [Revised: 08/18/2022] [Accepted: 08/27/2022] [Indexed: 11/16/2022] Open
Abstract
This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.
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26
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Li W, Yang L, Qiu Y, Yuan Y, Li X, Meng Z. FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis. BMC Bioinformatics 2022; 23:347. [PMID: 35986255 PMCID: PMC9392226 DOI: 10.1186/s12859-022-04889-3] [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: 05/30/2022] [Accepted: 08/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Amino acid property-aware phylogenetic analysis (APPA) refers to the phylogenetic analysis method based on amino acid property encoding, which is used for understanding and inferring evolutionary relationships between species from the molecular perspective. Fast Fourier transform (FFT) and Higuchi’s fractal dimension (HFD) have excellent performance in describing sequences’ structural and complexity information for APPA. However, with the exponential growth of protein sequence data, it is very important to develop a reliable APPA method for protein sequence analysis.
Results
Consequently, we propose a new method named FFP, it joints FFT and HFD. Firstly, FFP is used to encode protein sequences on the basis of the important physicochemical properties of amino acids, the dissociation constant, which determines acidity and basicity of protein molecules. Secondly, FFT and HFD are used to generate the feature vectors of encoded sequences, whereafter, the distance matrix is calculated from the cosine function, which describes the degree of similarity between species. The smaller the distance between them, the more similar they are. Finally, the phylogenetic tree is constructed. When FFP is tested for phylogenetic analysis on four groups of protein sequences, the results are obviously better than other comparisons, with the highest accuracy up to more than 97%.
Conclusion
FFP has higher accuracy in APPA and multi-sequence alignment. It also can measure the protein sequence similarity effectively. And it is hoped to play a role in APPA’s related research.
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27
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Tok S, Maurin H, Delay C, Crauwels D, Manyakov NV, Van Der Elst W, Moechars D, Drinkenburg WHIM. Pathological and neurophysiological outcomes of seeding human-derived tau pathology in the APP-KI NL-G-F and NL-NL mouse models of Alzheimer's Disease. Acta Neuropathol Commun 2022; 10:92. [PMID: 35739575 PMCID: PMC9219251 DOI: 10.1186/s40478-022-01393-w] [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: 04/27/2022] [Accepted: 06/07/2022] [Indexed: 12/02/2022] Open
Abstract
The two main histopathological hallmarks that characterize Alzheimer’s Disease are the presence of amyloid plaques and neurofibrillary tangles. One of the current approaches to studying the consequences of amyloid pathology relies on the usage of transgenic animal models that incorporate the mutant humanized form of the amyloid precursor protein (hAPP), with animal models progressively developing amyloid pathology as they age. However, these mice models generally overexpress the hAPP protein to facilitate the development of amyloid pathology, which has been suggested to elicit pathological and neuropathological changes unrelated to amyloid pathology. In this current study, we characterized APP knock-in (APP-KI) animals, that do not overexpress hAPP but still develop amyloid pathology to understand the influence of protein overexpression. We also induced tau pathology via human-derived tau seeding material to understand the neurophysiological effects of amyloid and tau pathology. We report that tau-seeded APP-KI animals progressively develop tau pathology, exacerbated by the presence of amyloid pathology. Interestingly, older amyloid-bearing, tau-seeded animals exhibited more amyloid pathology in the entorhinal area, isocortex and hippocampus, but not thalamus, which appeared to correlate with impairments in gamma oscillations before seeding. Tau-seeded animals also featured immediate deficits in power spectra values and phase-amplitude indices in the hippocampus after seeding, with gamma power spectra deficits persisting in younger animals. Both deficits in hippocampal phase-amplitude coupling and gamma power differentiate tau-seeded, amyloid-positive animals from buffer controls. Based on our results, impairments in gamma oscillations appear to be strongly associated with the presence and development of amyloid and tau pathology, and may also be an indicator of neuropathology, network dysfunction, and even potential disposition to the future development of amyloid pathology.
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Affiliation(s)
- S Tok
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium.,Groningen Institute for Evolutionary Life Sciences, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - H Maurin
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - C Delay
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - D Crauwels
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - N V Manyakov
- Data Sciences, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - W Van Der Elst
- Quantitative Sciences Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - D Moechars
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - W H I M Drinkenburg
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium. .,Groningen Institute for Evolutionary Life Sciences, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands.
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28
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Das S, Puthankattil SD. Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease. Front Comput Neurosci 2022; 16:877912. [PMID: 35733555 PMCID: PMC9207343 DOI: 10.3389/fncom.2022.877912] [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: 02/17/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundFunctional connectivity and complexity analysis has been discretely studied to understand intricate brain dynamics. The current study investigates the interplay between functional connectivity and complexity using the Kuramoto mean-field model.MethodFunctional connectivity matrices are estimated using the weighted phase lag index and complexity measures through popularly used complexity estimators such as Lempel-Ziv complexity (LZC), Higuchi's fractal dimension (HFD), and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated electroencephalogram (EEG) signals of patients with mild cognitive-impaired Alzheimer's disease (MCI-AD) and controls. Complexity measures are further applied to simulated signals generated from lesion-induced connectivity matrix and studied its impact. It is a novel attempt to study the relation between functional connectivity and complexity using a neurocomputational model.ResultsReal EEG signals from patients with MCI-AD exhibited reduced functional connectivity and complexity in anterior and central regions. A simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. A similar reduction in complexity was further evident in simulation studies with lesion-induced control groups compared with non-lesion-induced control groups.ConclusionTaken together, simulation studies demonstrate a positive influence of reduced connectivity in the model imparting a reduced complexity in the EEG signal. The study revealed the presence of a direct relation between functional connectivity and complexity with reduced connectivity, yielding a decreased EEG complexity.
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29
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Niu Y, Sun J, Wang B, Yang Y, Wen X, Xiang J. Trajectories of brain entropy across lifetime estimated by resting state functional magnetic resonance imaging. Hum Brain Mapp 2022; 43:4359-4369. [PMID: 35615859 PMCID: PMC9435012 DOI: 10.1002/hbm.25959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 04/07/2022] [Accepted: 04/10/2022] [Indexed: 11/25/2022] Open
Abstract
The human brain is a complex system of interconnected brain regions that form functional networks with differing roles in cognition and behavior. However, the trajectories of these functional networks across development are unclear and designing a metric to track the complex trajectory of these characteristics throughout the lifespan is challenging. Here, permutation entropy (PE) was used to examine age‐related variations in functional magnetic resonance imaging (fMRI) in healthy subjects aged 6–85 from global, network, and nodal perspectives. The global PE followed an inverted U‐shaped trajectory that peaked at approximately age 40. The trajectory of the motor and somatosensory functional network was more consistent with a linear model and increased with age; other functional networks showed inverted U‐shaped trajectories that peaked between 25 and 52 years of age. All nodes showed inverted U‐shaped trajectories. Using cluster analysis, the peak ages of nodes were grouped into three clusters (at 24, 38, and 51 years). Overall, we characterized four aging trajectories: networks with a linear increase, early peak age, intermediate peak age, and older peak age. These findings suggest possible complexity in trajectories at critical age points regarding changes in related functional brain networks.
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Affiliation(s)
- Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Sun
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xin Wen
- College of Software, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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30
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Borri A, Cerasa A, Tonin P, Citrigno L, Porcaro C. Characterizing Fractal Genetic Variation in the Human Genome from the Hapmap Project. Int J Neural Syst 2022; 32:2250028. [PMID: 35579974 DOI: 10.1142/s0129065722500289] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Over the last decades, the exuberant development of next-generation sequencing has revolutionized gene discovery. These technologies have boosted the mapping of single nucleotide polymorphisms (SNPs) across the human genome, providing a complex universe of heterogeneity characterizing individuals worldwide. Fractal dimension (FD) measures the degree of geometric irregularity, quantifying how "complex" a self-similar natural phenomenon is. We compared two FD algorithms, box-counting dimension (BCD) and Higuchi's fractal dimension (HFD), to characterize genome-wide patterns of SNPs extracted from the HapMap data set, which includes data from 1184 healthy subjects of eleven populations. In addition, we have used cluster and classification analysis to relate the genetic distances within chromosomes based on FD similarities to the geographical distances among the 11 global populations. We found that HFD outperformed BCD at both grand average clusterization analysis by the cophenetic correlation coefficient, in which the closest value to 1 represents the most accurate clustering solution (0.981 for the HFD and 0.956 for the BCD) and classification (79.0% accuracy, 61.7% sensitivity, and 96.4% specificity for the HFD with respect to 69.1% accuracy, 43.2% sensitivity, and 94.9% specificity for the BCD) of the 11 populations present in the HapMap data set. These results support the evidence that HFD is a reliable measure helpful in representing individual variations within all chromosomes and categorizing individuals and global populations.
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Affiliation(s)
- Alessandro Borri
- CNR-IASI Biomathematics Laboratory, (BioMatLab), Rome, Italy.,Centre of Excellence for Research DEWS, University of L'Aquila, L'Aquila, Italy
| | - Antonio Cerasa
- Institute for Biomedical Research and Innovation, National Research Council, IRIB-CNR, 98164 Messina, Italy.,S. Anna Institute, 88900 Crotone, Italy.,Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Arcavacata, Italy
| | | | - Luigi Citrigno
- Institute for Biomedical Research and Innovation, National Research Council, IRIB-CNR, 87050 Mangone CS, Italy
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience, Center (PNC), University of Padova, Padova, Italy.,Institute of Cognitive Sciences and Technologies, (ISTC) - National Research Council (CNR), Rome, Italy.,Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
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31
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Tok S, Maurin H, Delay C, Crauwels D, Manyakov NV, Van Der Elst W, Moechars D, Drinkenburg WHIM. Neurophysiological effects of human-derived pathological tau conformers in the APPKM670/671NL.PS1/L166P amyloid mouse model of Alzheimer's disease. Sci Rep 2022; 12:7784. [PMID: 35546164 PMCID: PMC9094605 DOI: 10.1038/s41598-022-11582-1] [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: 12/30/2021] [Accepted: 04/19/2022] [Indexed: 11/09/2022] Open
Abstract
Alzheimer’s Disease (AD) is a neurodegenerative disease characterized by two main pathological hallmarks: amyloid plaques and intracellular tau neurofibrillary tangles. However, a majority of studies focus on the individual pathologies and seldom on the interaction between the two pathologies. Herein, we present the longitudinal neuropathological and neurophysiological effects of a combined amyloid-tau model by hippocampal seeding of human-derived tau pathology in the APP.PS1/L166P amyloid animal model. We statistically assessed both neurophysiological and pathological changes using linear mixed modelling to determine if factors such as the age at which animals were seeded, genotype, seeding or buffer, brain region where pathology was quantified, and time-post injection differentially affect these outcomes. We report that AT8-positive tau pathology progressively develops and is facilitated by the amount of amyloid pathology present at the time of injection. The amount of AT8-positive tau pathology was influenced by the interaction of age at which the animal was injected, genotype, and time after injection. Baseline pathology-related power spectra and Higuchi Fractal Dimension (HFD) score alterations were noted in APP.PS1/L166P before any manipulations were performed, indicating a baseline difference associated with genotype. We also report immediate localized hippocampal dysfunction in the electroencephalography (EEG) power spectra associated with tau seeding which returned to comparable levels at 1 month-post-injection. Longitudinal effects of seeding indicated that tau-seeded wild-type mice showed an increase in gamma power earlier than buffer control comparisons which was influenced by the age at which the animal was injected. A reduction of hippocampal broadband power spectra was noted in tau-seeded wild-type mice, but absent in APP.PS1 animals. HFD scores appeared to detect subtle effects associated with tau seeding in APP.PS1 animals, which was differentially influenced by genotype. Notably, while tau histopathological changes were present, a lack of overt longitudinal electrophysiological alterations was noted, particularly in APP.PS1 animals that feature both pathologies after seeding, reiterating and underscoring the difficulty and complexity associated with elucidating physiologically relevant and translatable biomarkers of Alzheimer’s Disease at the early stages of the disease.
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Affiliation(s)
- S Tok
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium. .,Faculty of Science and Engineering, Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.
| | - H Maurin
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - C Delay
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - D Crauwels
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - N V Manyakov
- Data Sciences, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - W Van Der Elst
- Quantitative Sciences Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - D Moechars
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - W H I M Drinkenburg
- Department of Neuroscience, Janssen Research and Development, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium.,Faculty of Science and Engineering, Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
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32
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Porcaro C, Marino M, Carozzo S, Russo M, Ursino M, Valentinaruggiero, Ragno C, Proto S, Tonin P. Fractal Dimension Feature as a Signature of Severity in Disorders of Consciousness: An EEG Study. Int J Neural Syst 2022; 32:2250031. [DOI: 10.1142/s0129065722500319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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33
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Tran Y, Austin P, Lo C, Craig A, Middleton JW, Wrigley PJ, Siddall P. An Exploratory EEG Analysis on the Effects of Virtual Reality in People with Neuropathic Pain Following Spinal Cord Injury. SENSORS (BASEL, SWITZERLAND) 2022; 22:2629. [PMID: 35408245 PMCID: PMC9002545 DOI: 10.3390/s22072629] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 03/27/2022] [Accepted: 03/27/2022] [Indexed: 02/01/2023]
Abstract
Neuropathic pain in people with spinal cord injury is thought to be due to altered central neuronal activity. A novel therapeutic intervention using virtual reality (VR) head-mounted devices was investigated in this study for pain relief. Given the potential links to neuronal activity, the aim of the current study was to determine whether use of VR was associated with corresponding changes in electroencephalography (EEG) patterns linked to the presence of neuropathic pain. Using a within-subject, randomised cross-over pilot trial, we compared EEG activity for three conditions: no task eyes open state, 2D screen task and 3D VR task. We found an increase in delta activity in frontal regions for 3D VR with a decrease in theta activity. There was also a consistent decrease in relative alpha band (8-12 Hz) and an increase in low gamma (30-45 Hz) power during 2D screen and 3D VR corresponding, with reduced self-reported pain. Using the nonlinear and non-oscillatory method of extracting fractal dimensions, we found increases in brain complexity during 2D screen and 3D VR. We successfully classified the 3D VR condition from 2D screen and eyes opened no task conditions with an overall accuracy of 80.3%. The findings in this study have implications for using VR applications as a therapeutic intervention for neuropathic pain in people with spinal cord injury.
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Affiliation(s)
- Yvonne Tran
- Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
| | - Philip Austin
- Department of Pain Management, HammondCare, Greenwich Hospital Greenwich, Sydney, NSW 2065, Australia; (P.A.); (P.S.)
| | - Charles Lo
- Management Disciplinary Group, Wentworth Institute of Higher Education, Surrey Hills, NSW 2010, Australia;
| | - Ashley Craig
- Sydney Medical School-Northern, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (A.C.); (J.W.M.); (P.J.W.)
- John Walsh Centre for Rehabilitation Research, Kolling Institute, Northern Sydney Local Health District, St Leonards, NSW 2065, Australia
| | - James W. Middleton
- Sydney Medical School-Northern, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (A.C.); (J.W.M.); (P.J.W.)
- John Walsh Centre for Rehabilitation Research, Kolling Institute, Northern Sydney Local Health District, St Leonards, NSW 2065, Australia
| | - Paul J. Wrigley
- Sydney Medical School-Northern, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (A.C.); (J.W.M.); (P.J.W.)
- Pain Management Research Institute, Kolling Institute, Northern Sydney Local Health District, St Leonards, NSW 2065, Australia
| | - Philip Siddall
- Department of Pain Management, HammondCare, Greenwich Hospital Greenwich, Sydney, NSW 2065, Australia; (P.A.); (P.S.)
- Sydney Medical School-Northern, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (A.C.); (J.W.M.); (P.J.W.)
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Gu C, Liu ZX, Woltering S. Electroencephalography complexity in resting and task states in adults with attention-deficit/hyperactivity disorder. Brain Commun 2022; 4:fcac054. [PMID: 35368615 PMCID: PMC8971899 DOI: 10.1093/braincomms/fcac054] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 12/19/2021] [Accepted: 03/04/2022] [Indexed: 11/15/2022] Open
Abstract
Analysing EEG complexity could provide insight into neural connectivity underlying attention-deficit/hyperactivity disorder symptoms. EEG complexity was calculated through multiscale entropy and compared between adults with attention-deficit/hyperactivity disorder and their peers during resting and go/nogo task states. Multiscale entropy change from the resting state to the task state was also examined as an index of the brain’s ability to change from a resting to an active state. Thirty unmedicated adults with attention-deficit/hyperactivity disorder were compared with 30 match-paired healthy peers on the multiscale entropy in the resting and task states as well as their multiscale entropy change. Results showed differences in multiscale entropy between individuals with attention-deficit/hyperactivity disorder and their peers during the resting state as well as the task state. The multiscale entropy measured from the comparison group was larger than that from the attention-deficit/hyperactivity disorder group in the resting state, whereas the reverse pattern was found during the task state. Our most robust finding showed that the multiscale entropy change from individuals with attention-deficit/hyperactivity disorder was smaller than that from their peers, specifically at frontal sites. Interestingly, individuals without attention-deficit/hyperactivity disorder performed better with decreasing multiscale entropy changes, demonstrating higher accuracy, faster reaction time and less variability in their reaction times. These data suggest that multiscale entropy could not only provide insight into neural connectivity differences between adults with attention-deficit/hyperactivity disorder and their peers but also into their behavioural performance.
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Affiliation(s)
- Chao Gu
- Department of Neuroscience, Texas A&M University, USA
- Department of Psychiatry, Massachusetts General Hospital, USA
| | - Zhong-Xu Liu
- Department of Behavioral Sciences, University of Michigan-Dearborn, USA
| | - Steven Woltering
- Department of Educational Psychology, Texas A&M University, USA
- Department of Applied Psychology and Human Development, University of Toronto, Canada
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35
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Rossini PM, Miraglia F, Vecchio F, Di Iorio R, Iodice F, Cotelli M. General principles of brain electromagnetic rhythmic oscillations and implications for neuroplasticity. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:221-237. [PMID: 35034737 DOI: 10.1016/b978-0-12-819410-2.00012-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Neuro-plasticity describes the ability of the brain in achieving novel functions, either by transforming its internal connectivity, or by changing the elements of which it is made, meaning that, only those changes, that affect both structural and functional aspects of the system, can be defined as "plastic." The concept of plasticity can be applied to molecular as well as to environmental events that can be recognized as the basic mechanism by which our brain reacts to the internal and external stimuli. When considering brain plasticity within a clinical context-that is the process linked with changes of brain functions following a lesion- the term "reorganization" is somewhat synonymous, referring to the specific types of structural/functional modifications observed as axonal sprouting, long-term synaptic potentiation/inhibition or to the plasticity related genomic responses. Furthermore, brain rewires during maturation, and aging thus maintaining a remarkable learning capacity, allowing it to acquire a wide range of skills, from motor actions to complex abstract reasoning, in a lifelong expression. In this review, the contribution on the "neuroplasticity" topic coming from advanced analysis of EEG rhythms is put forward.
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Affiliation(s)
- Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy; Department of Technical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | | | - Francesco Iodice
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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Porcaro C, Nemirovsky IE, Riganello F, Mansour Z, Cerasa A, Tonin P, Stojanoski B, Soddu A. Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State. Front Neurol 2022; 12:778951. [PMID: 35095725 PMCID: PMC8793804 DOI: 10.3389/fneur.2021.778951] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/08/2021] [Indexed: 12/12/2022] Open
Abstract
When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Institute of Cognitive Sciences and Technologies (ISTC)–National Research Council (CNR), Rome, Italy
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
- *Correspondence: Camillo Porcaro ; orcid.org/0000-0003-4847-163X
| | - Idan Efim Nemirovsky
- Department of Physics and Astronomy, Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Francesco Riganello
- Sant'Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy
| | - Zahra Mansour
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Antonio Cerasa
- Sant'Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy
- Institute for Biomedical Research and Innovation (IRIB), National Research Council, Messina, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, Rende, Italy
| | - Paolo Tonin
- Sant'Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy
| | - Bobby Stojanoski
- Faculty of Social Science and Humanities, University of Ontario Institute of Technology, Oshawa, ON, Canada
- Department of Psychology, Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Andrea Soddu
- Department of Physics and Astronomy, Brain and Mind Institute, University of Western Ontario, London, ON, Canada
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Olejarczyk E, Gotman J, Frauscher B. Region-specific complexity of the intracranial EEG in the sleeping human brain. Sci Rep 2022; 12:451. [PMID: 35013431 PMCID: PMC8748934 DOI: 10.1038/s41598-021-04213-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 12/13/2021] [Indexed: 11/18/2022] Open
Abstract
As the brain is a complex system with occurrence of self-similarity at different levels, a dedicated analysis of the complexity of brain signals is of interest to elucidate the functional role of various brain regions across the various stages of vigilance. We exploited intracranial electroencephalogram data from 38 cortical regions using the Higuchi fractal dimension (HFD) as measure to assess brain complexity, on a dataset of 1772 electrode locations. HFD values depended on sleep stage and topography. HFD increased with higher levels of vigilance, being highest during wakefulness in the frontal lobe. HFD did not change from wake to stage N2 in temporo-occipital regions. The transverse temporal gyrus was the only area in which the HFD did not differ between any two vigilance stages. Interestingly, HFD of wakefulness and stage R were different mainly in the precentral gyrus, possibly reflecting motor inhibition in stage R. The fusiform and parahippocampal gyri were the only areas showing no difference between wakefulness and N2. Stages R and N2 were similar only for the postcentral gyrus. Topographical analysis of brain complexity revealed that sleep stages are clearly differentiated in fronto-central brain regions, but that temporo-occipital regions sleep differently.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Trojdena 4 Str., 02-109, Warsaw, Poland.
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
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38
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Decision Tree in Working Memory Task Effectively Characterizes EEG Signals in Healthy Aging Adults. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Armonaite K, Bertoli M, Paulon L, Gianni E, Balsi M, Conti L, Tecchio F. Neuronal Electrical Ongoing Activity as Cortical Areas Signature: An Insight from MNI Intracerebral Recording Atlas. Cereb Cortex 2021; 32:2895-2906. [PMID: 34727186 DOI: 10.1093/cercor/bhab389] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/14/2022] Open
Abstract
The time course of the neuronal activity in the brain network, the neurodynamics, reflects the structure and functionality of the generating neuronal pools. Here, using the intracranial stereo-electroencephalographic (sEEG) recordings of the public Montreal Neurological Institute (MNI) atlas, we investigated the neurodynamics of primary motor (M1), somatosensory (S1) and auditory (A1) cortices measuring power spectral densities (PSD) and Higuchi fractal dimension (HFD) in the same subject (M1 vs. S1 in 16 subjects, M1 vs. A1 in 9, S1 vs. A1 in 6). We observed specific spectral features in M1, which prevailed above beta band, S1 in the alpha band, and A1 in the delta band. M1 HFD was higher than S1, both higher than A1. A clear distinction of neurodynamics properties of specific primary cortices supports the efforts in cortical parceling based on this expression of the local cytoarchitecture and connectivity. In this perspective, we selected within the MNI intracortical database a first set of primary motor, somatosensory and auditory cortices' representatives to query in recognizing ongoing patterns of neuronal communication. Potential clinical impact stands primarily in exploiting such exchange patterns to enhance the efficacy of neuromodulation intervention to cure symptoms secondary to neuronal activity unbalances.
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Affiliation(s)
| | - Massimo Bertoli
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy.,Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele D'Annunzio' of Chieti-Pescara, Chieti 66100, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy
| | - Eugenia Gianni
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy.,Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome 00128, Italy
| | - Marco Balsi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University, Rome 00185, Italy
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Rome 00186, Italy.,INFN - Istituto Nazionale di Fisica Nucleare, Sezione Roma Tor Vergata, Rome 00133, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy
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40
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Porcaro C, Di Renzo A, Tinelli E, Di Lorenzo G, Seri S, Di Lorenzo C, Parisi V, Caramia F, Fiorelli M, Di Piero V, Pierelli F, Coppola G. Hypothalamic structural integrity and temporal complexity of cortical information processing at rest in migraine without aura patients between attacks. Sci Rep 2021; 11:18701. [PMID: 34548562 PMCID: PMC8455544 DOI: 10.1038/s41598-021-98213-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/30/2021] [Indexed: 02/08/2023] Open
Abstract
The hypothalamus has been attributed an important role during the premonitory phase of a migraine attack. Less is known about the role played by the hypothalamus in the interictal period and its relationship with the putative neurocognitive networks previously identified in the pathophysiology of migraine. Our aim was to test whether the hypothalamic microstructure would be altered during the interictal period and whether this co-existed with aberrant connectivity at cortical level. We collected multimodal MRI data from 20 untreated patients with migraine without aura between attacks (MO) and 20 healthy controls (HC) and studied fractional anisotropy, mean (MD), radial (RD), and axial diffusivity of the hypothalamus ROI as a whole from diffusion tensor imaging (DTI). Moreover, we performed an exploratory analysis of the same DTI metrics separately for the anterior and posterior hypothalamic ROIs bilaterally. From resting-state functional MRI, we estimated the Higuchi's fractal dimension (FD), an index of temporal complexity sensible to describe non-periodic patterns characterizing BOLD signature. Finally, we correlated neuroimaging findings with migraine clinical features. In comparison to HC, MO had significantly higher MD, AD, and RD values within the hypothalamus. These findings were confirmed also in the exploratory analysis on the sub-regions of the hypothalamus bilaterally, with the addition of lower FA values on the posterior ROIs. Patients showed higher FD values within the salience network (SN) and the cerebellum, and lower FD values within the primary visual (PV) network compared to HC. We found a positive correlation between cerebellar and SN FD values and severity of migraine. Our findings of hypothalamic abnormalities between migraine attacks may form part of the neuroanatomical substrate that predisposes the onset of the prodromal phase and, therefore, the initiation of an attack. The peculiar fractal dimensionality we found in PV, SN, and cerebellum may be interpreted as an expression of abnormal efficiency demand of brain networks devoted to the integration of sensory, emotional, and cognitive information related to the severity of migraine.
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Affiliation(s)
- Camillo Porcaro
- grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR), Rome, Italy ,grid.6572.60000 0004 1936 7486Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK ,S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy ,grid.7010.60000 0001 1017 3210Department of Information Engineering - Università Politecnica delle Marche, Ancona, Italy
| | | | - Emanuele Tinelli
- grid.7841.aDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Giorgio Di Lorenzo
- grid.6530.00000 0001 2300 0941Laboratory of Psychophysiology and Cognitive Neuroscience, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy ,grid.417778.a0000 0001 0692 3437IRCCS - Fondazione Santa Lucia, Rome, Italy
| | - Stefano Seri
- grid.7273.10000 0004 0376 4727College of Health and Life Sciences, Aston Institute of Health and Neurodevelopment, Aston University, Birmingham, UK ,grid.498025.2Department of Clinical Neurophysiology, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK
| | - Cherubino Di Lorenzo
- grid.7841.aDepartment of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Corso della Repubblica 79, 04100 Latina, Italy
| | | | - Francesca Caramia
- grid.7841.aDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Marco Fiorelli
- grid.7841.aDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Vittorio Di Piero
- grid.7841.aDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Francesco Pierelli
- grid.7841.aDepartment of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Corso della Repubblica 79, 04100 Latina, Italy ,grid.419543.e0000 0004 1760 3561IRCCS - Neuromed, Pozzilli, IS Italy
| | - Gianluca Coppola
- grid.7841.aDepartment of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Corso della Repubblica 79, 04100 Latina, Italy
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Anderson K, Chirion C, Fraser M, Purcell M, Stein S, Vuckovic A. Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury. Front Neurosci 2021; 15:705652. [PMID: 34512243 PMCID: PMC8427815 DOI: 10.3389/fnins.2021.705652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/31/2021] [Indexed: 11/18/2022] Open
Abstract
Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.
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Affiliation(s)
- Keri Anderson
- Biomedical Engineering Division, University of Glasgow, Glasgow, United Kingdom
| | - Cristian Chirion
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Matthew Fraser
- Queen Elizabeth National Spinal Injuries Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Sebastian Stein
- School of Computing Science, University of Glasgow, Glasgow, United Kingdom
| | - Aleksandra Vuckovic
- Biomedical Engineering Division, University of Glasgow, Glasgow, United Kingdom
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Al-Nuaimi AH, Blūma M, Al-Juboori SS, Eke CS, Jammeh E, Sun L, Ifeachor E. Robust EEG Based Biomarkers to Detect Alzheimer's Disease. Brain Sci 2021; 11:1026. [PMID: 34439645 PMCID: PMC8394244 DOI: 10.3390/brainsci11081026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022] Open
Abstract
Biomarkers to detect Alzheimer's disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).
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Affiliation(s)
- Ali H. Al-Nuaimi
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Marina Blūma
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Shaymaa S. Al-Juboori
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Chima S. Eke
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Jammeh
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Lingfen Sun
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Ifeachor
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
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Abstract
Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.
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44
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Ando M, Nobukawa S, Kikuchi M, Takahashi T. Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis. Front Neurosci 2021; 15:667614. [PMID: 34262427 PMCID: PMC8273283 DOI: 10.3389/fnins.2021.667614] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are emphasized. As a diagnostic index for AD patients, evaluating the complexity of the dependence of the electroencephalography (EEG) signal on the temporal scale of Alzheimer's disease (AD) patients is effective. Multiscale entropy analysis and multifractal analysis have been performed individually, and their usefulness as diagnostic indicators has been confirmed, but the complemental relationship between these analyses, which may enhance diagnostic accuracy, has not been investigated. We hypothesize that combining multiscale entropy and fractal analyses may add another dimension to understanding the alteration of EEG dynamics in AD. In this study, we performed both multiscale entropy and multifractal analyses on EEGs from AD patients and healthy subjects. We found that the classification accuracy was improved using both techniques. These findings suggest that the use of multiscale entropy analysis and multifractal analysis may lead to the development of AD diagnostic tools.
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Affiliation(s)
- Momo Ando
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology, Narashino, Japan.,Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Behavioral Science, Kanazawa University, Ishikawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Ishikawa, Japan.,Department of Neuropsychiatry, University of Fukui, Fukui, Japan.,Uozu Shinkei Sanatorium, Uozu, Japan
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45
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Tecchio F, Bertoli M, Gianni E, L'Abbate T, Paulon L, Zappasodi F. To Be Is To Become. Fractal Neurodynamics of the Body-Brain Control System. Front Physiol 2021; 11:609768. [PMID: 33384616 PMCID: PMC7770125 DOI: 10.3389/fphys.2020.609768] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 11/25/2020] [Indexed: 11/28/2022] Open
Affiliation(s)
- Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Massimo Bertoli
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy.,Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
| | - Eugenia Gianni
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy.,Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Teresa L'Abbate
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience, Institute of Cognitive Sciences and Technologies-Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele d'Annunzio' of Chieti-Pescara, Chieti, Italy
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46
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Cuenoud B, Ipek Ö, Shevlyakova M, Beaumont M, Cunnane SC, Gruetter R, Xin L. Brain NAD Is Associated With ATP Energy Production and Membrane Phospholipid Turnover in Humans. Front Aging Neurosci 2020; 12:609517. [PMID: 33390929 PMCID: PMC7772416 DOI: 10.3389/fnagi.2020.609517] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 11/23/2020] [Indexed: 12/16/2022] Open
Abstract
The brain requires a large amount of energy, mostly derived from the metabolism of glucose, which decreases substantially with age and neurological diseases. While mounting evidence in model organisms illustrates the central role of brain nicotinamide adenine dinucleotide (NAD) for maintaining energy homeostasis, similar data are sparse in humans. This study explores the correlations between brain NAD, energy production and membrane phospholipid metabolism by 31-phosphorous magnetic resonance spectroscopy (31P-MRS) across 50 healthy participants including a young (mean age 27.1-year-old) and middle-aged (mean age 56.4-year-old) group. The analysis revealed that brain NAD level and NAD+/NADH redox ratio were positively associated with ATP level and the rate of energy production, respectively. Moreover, a metabolic network linking NAD with membrane phospholipid metabolism, energy production, and aging was identified. An inverted trend between age and NAD level was detected. These results pave the way for the use of 31P-MRS as a powerful non-invasive tool to support the development of new therapeutic interventions targeting NAD associated phospho-metabolic pathways in brain aging and neurological diseases.
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Affiliation(s)
| | - Özlem Ipek
- School of Biomedical Imaging & Imaging Sciences, King's College London, London, United Kingdom
| | - Maya Shevlyakova
- Clinical Development Unit, Nestlé Research Center, Lausanne, Switzerland
| | - Maurice Beaumont
- Clinical Development Unit, Nestlé Research Center, Lausanne, Switzerland
| | - Stephen C Cunnane
- Department of Medicine, Université de Sherbrooke and Research Center on Aging, Sherbrooke, QC, Canada
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lijing Xin
- Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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47
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Porcaro C, Mayhew SD, Marino M, Mantini D, Bagshaw AP. Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis. Int J Neural Syst 2020; 30:2050061. [PMID: 33034533 DOI: 10.1142/s0129065720500616] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Intrinsic brain activity is organized into large-scale networks displaying specific structural-functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatial and temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.
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Affiliation(s)
- Camillo Porcaro
- Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR) Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.,S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy.,Department of Information Engineering - Università, Politecnica delle Marche, Ancona, Italy.,Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Stephen D Mayhew
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Andrew P Bagshaw
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
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48
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Santuz A, Ekizos A, Kunimasa Y, Kijima K, Ishikawa M, Arampatzis A. Lower complexity of motor primitives ensures robust control of high-speed human locomotion. Heliyon 2020; 6:e05377. [PMID: 33163662 PMCID: PMC7610320 DOI: 10.1016/j.heliyon.2020.e05377] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/15/2020] [Accepted: 10/27/2020] [Indexed: 01/06/2023] Open
Abstract
Walking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we challenged human locomotion by having our participants walk and run at a very broad spectrum of submaximal and maximal speeds. The synergistic activations of lower limb locomotor muscles were obtained through decomposition of electromyographic data via non-negative matrix factorization. We analyzed the duration and complexity (via fractal analysis) over time of motor primitives, the temporal components of muscle synergies. We found that the motor control of high-speed locomotion was so challenging that the neuromotor system was forced to produce wider and less complex muscle activation patterns. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that humans cope with the challenges of high-speed locomotion by adapting the neuromotor dynamics through a set of strategies that allow for efficient creation and control of locomotion.
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Affiliation(s)
- Alessandro Santuz
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
- Berlin School of Movement Science, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Antonis Ekizos
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
- Berlin School of Movement Science, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
| | - Yoko Kunimasa
- Graduate School of Sport and Exercise Sciences, Osaka University of Health and Sport Sciences, 590-0459 Osaka, Japan
| | - Kota Kijima
- Graduate School of Sport and Exercise Sciences, Osaka University of Health and Sport Sciences, 590-0459 Osaka, Japan
| | - Masaki Ishikawa
- Graduate School of Sport and Exercise Sciences, Osaka University of Health and Sport Sciences, 590-0459 Osaka, Japan
| | - Adamantios Arampatzis
- Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
- Berlin School of Movement Science, Humboldt-Universität zu Berlin, 10115 Berlin, Germany
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49
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Porcaro C, Di Renzo A, Tinelli E, Di Lorenzo G, Parisi V, Caramia F, Fiorelli M, Di Piero V, Pierelli F, Coppola G. Haemodynamic activity characterization of resting state networks by fractal analysis and thalamocortical morphofunctional integrity in chronic migraine. J Headache Pain 2020; 21:112. [PMID: 32928129 PMCID: PMC7490862 DOI: 10.1186/s10194-020-01181-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/08/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Chronic migraine (CM) can be associated with aberrant long-range connectivity of MRI-derived resting-state networks (RSNs). Here, we investigated how the fractal dimension (FD) of blood oxygenation level dependent (BOLD) activity may be used to estimate the complexity of RSNs, reflecting flexibility and/or efficiency in information processing in CM patients respect to healthy controls (HC). METHODS Resting-state MRI data were collected from 20 untreated CM without history of medication overuse and 20 HC. On both groups, we estimated the Higuchi's FD. On the same subjects, fractional anisotropy (FA) and mean diffusivity (MD) values of bilateral thalami were retrieved from diffusion tensor imaging and correlated with the FD values. RESULTS CM showed higher FD values within dorsal attention system (DAS) and the anterior part of default-mode network (DMN), and lower FD values within the posterior DMN compared to HC. Although FA and MD were within the range of normality, both correlated with the FD values of DAS. CONCLUSIONS FD of DAS and DMN may reflect disruption of cognitive control of pain in CM. Since the normal microstructure of the thalamus and its positive connectivity with the cortical networking found in our CM patients reminds similar results obtained assessing the same structures but with the methods of neurophysiology, in episodic migraine during an attack, this may be yet another evidence in supporting CM as a never-ending migraine attack.
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Affiliation(s)
- Camillo Porcaro
- Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Via Palestro 32, I-00185, Rome, Italy.
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.
- S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy.
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.
| | | | - Emanuele Tinelli
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Giorgio Di Lorenzo
- Laboratory of Psychophysiology and Cognitive Neuroscience, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
- IRCCS Fondazione Santa Lucia, Rome, Italy
| | | | - Francesca Caramia
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Marco Fiorelli
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Vittorio Di Piero
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Francesco Pierelli
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Latina, Italy
- IRCCS - Neuromed, Pozzilli, (IS), Italy
| | - Gianluca Coppola
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome Polo Pontino, Latina, Italy
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50
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Santuz A, Akay T. Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them. J Neurophysiol 2020; 124:1083-1091. [PMID: 32816603 DOI: 10.1152/jn.00360.2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Time-dependent physiological data sets are often difficult to interpret objectively. Biosignals such as electromyogram, electroencephalogram, or single-neuron recordings can be interpreted using various linear and nonlinear methods. Each analysis technique aims at the explanation of different data features that might be visible or not to the naked eye. Here, we used linear decomposition based on machine learning to extract motor primitives (the time-dependent coefficients of muscle synergies) from the hindlimb electromyographic activity of mice during normal and mechanically perturbed locomotion. We set out to investigate the effects of calculation parameters and data quality on two nonlinear metrics derived from fractal analysis: the Higuchi's fractal dimension (HFD) and the Hurst exponent (H). Both HFD and H proved to be exceptionally sensitive to changes in motor primitives induced by external perturbations to locomotion. We discuss the potential pitfalls that might arise from fractal analysis by using examples based on surrogate data. We conclude giving some simple, data-driven suggestions to reduce the chance of misinterpretations when metrics such as HFD and H are applied to any biological signal containing elements of periodicity.NEW & NOTEWORTHY Despite the lack of consensus on how to perform fractal analysis of physiological time series, many studies rely on this technique. Here, we shed light on the potential pitfalls of using the Higuchi's fractal dimension and the Hurst exponent. We expose and suggest how to solve the drawbacks of such methods when applied to data from normal and perturbed locomotion by combining in vivo recordings and computational approaches.
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Affiliation(s)
- Alessandro Santuz
- Atlantic Mobility Action Project, Brain Repair Centre, Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada.,Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin School of Movement Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Turgay Akay
- Atlantic Mobility Action Project, Brain Repair Centre, Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
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