1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Ludwig S, Bakas S, Adamos DA, Laskaris N, Panagakis Y, Zafeiriou S. EEGminer: discovering interpretable features of brain activity with learnable filters. J Neural Eng 2024; 21:036010. [PMID: 38684154 DOI: 10.1088/1741-2552/ad44d7] [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: 07/28/2023] [Accepted: 04/29/2024] [Indexed: 05/02/2024]
Abstract
Objective. The patterns of brain activity associated with different brain processes can be used to identify different brain states and make behavioural predictions. However, the relevant features are not readily apparent and accessible. Our aim is to design a system for learning informative latent representations from multichannel recordings of ongoing EEG activity.Approach: We propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates.Main results.We demonstrate the utility of our model on a new EEG dataset of unprecedented size (i.e. 721 subjects), where we identify consistent trends of music perception and related individual differences. Furthermore, we train and apply our model in two additional datasets, specifically for emotion recognition on SEED and workload classification on simultaneous task EEG workload. The discovered features align well with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the specialisation of the temporal lobes regarding music perception proposed in the literature.Significance. The proposed method offers strong interpretability of learned features while reaching similar levels of accuracy achieved by black box deep learning models. This improved trustworthiness may promote the use of deep learning models in real world applications. The model code is available athttps://github.com/SMLudwig/EEGminer/.
Collapse
Affiliation(s)
- Siegfried Ludwig
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Stylianos Bakas
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- School of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Cogitat Ltd, London, United Kingdom
| | - Dimitrios A Adamos
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| | - Nikolaos Laskaris
- School of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Cogitat Ltd, London, United Kingdom
| | - Yannis Panagakis
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens 15784, Greece
- Cogitat Ltd, London, United Kingdom
| | - Stefanos Zafeiriou
- Department of Computing, Imperial College London, London SW7 2RH, United Kingdom
- Cogitat Ltd, London, United Kingdom
| |
Collapse
|
3
|
Coronel-Oliveros C, Medel V, Whitaker GA, Astudillo A, Gallagher D, Z-Rivera L, Prado P, El-Deredy W, Orio P, Weinstein A. Elevating understanding: Linking high-altitude hypoxia to brain aging through EEG functional connectivity and spectral analyses. Netw Neurosci 2024; 8:275-292. [PMID: 38562297 PMCID: PMC10927308 DOI: 10.1162/netn_a_00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/17/2023] [Indexed: 04/04/2024] Open
Abstract
High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from 16 healthy volunteers during acute high-altitude hypoxia (at 4,000 masl) and at sea level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration using graph theory tools. High altitude led to slower brain oscillations, that is, increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.
Collapse
Affiliation(s)
- Carlos Coronel-Oliveros
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute (GBHI), University of California, San Francisco (UCSF), San Francisco, CA, USA and Trinity College Dublin, Dublin, Ireland
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Vicente Medel
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
- Department of Neuroscience, Universidad de Chile, Santiago, Chile
| | - Grace Alma Whitaker
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
- Chair of Acoustics and Haptics, Technische Universität Dresden, Dresden, Germany
| | - Aland Astudillo
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
- NICM Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia
| | - David Gallagher
- School of Psychology, Liverpool John Moores University, Liverpool, England
| | - Lucía Z-Rivera
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Wael El-Deredy
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
- Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Alejandro Weinstein
- Advanced Center for Electrical and Electronics Engineering (AC3E), Federico Santa María Technical University, Valparaíso, Chile
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| |
Collapse
|
4
|
da Silva Fiorin F, do Espírito Santo CC, Da Silva JT, Chung MK. Inflammation, brain connectivity, and neuromodulation in post-traumatic headache. Brain Behav Immun Health 2024; 35:100723. [PMID: 38292321 PMCID: PMC10827408 DOI: 10.1016/j.bbih.2024.100723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
Post-traumatic headache (PTH) is a debilitating condition that affects individuals with different levels of traumatic brain injury (TBI) severity. The difficulties in developing an effective treatment are related to a lack of understanding the complicated mechanisms and neurobiological changes in brain function after a brain injury. Preclinical studies have indicated that peripheral and central sensitization of the trigeminal nociceptive pathways contributes to PTH. While recent brain imaging studies have uncovered widespread changes in brain functional connectivity following trauma, understanding exactly how these networks contribute to PTH after injury remains unknown. Stimulation of peripheral (trigeminal or vagus) nerves show promising efficacies in PTH experimental animals, likely mediated by influencing TBI-induced pathological plasticity by decreasing neuroinflammation and neuronal apoptosis. Non-invasive brain stimulations, such as transcranial magnetic or direct current stimulations, show analgesia for multiple chronic pain conditions, including PTH. Better mechanistic understanding of analgesia achieved by neuromodulations can define peripheral and central mechanisms involved in the development, the resolution, and the management of PTH.
Collapse
Affiliation(s)
- Fernando da Silva Fiorin
- Department of Neural and Pain Sciences, School of Dentistry, University of Maryland Baltimore, Program in Neuroscience, Center to Advance Chronic Pain Research, Baltimore, MD, USA
| | - Caroline Cunha do Espírito Santo
- Graduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neuroscience, Santos Dumont Institute, Brazil
| | - Joyce T. Da Silva
- Department of Neural and Pain Sciences, School of Dentistry, University of Maryland Baltimore, Program in Neuroscience, Center to Advance Chronic Pain Research, Baltimore, MD, USA
| | - Man-Kyo Chung
- Department of Neural and Pain Sciences, School of Dentistry, University of Maryland Baltimore, Program in Neuroscience, Center to Advance Chronic Pain Research, Baltimore, MD, USA
| |
Collapse
|
5
|
Fourdain S, Provost S, Tremblay J, Vannasing P, Doussau A, Caron-Desrochers L, Gaudet I, Roger K, Hüsser A, Dehaes M, Martinez-Montes E, Poirier N, Gallagher A. Functional brain connectivity after corrective cardiac surgery for critical congenital heart disease: a preliminary near-infrared spectroscopy (NIRS) report. Child Neuropsychol 2023; 29:1088-1108. [PMID: 36718095 DOI: 10.1080/09297049.2023.2170340] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 01/13/2023] [Indexed: 02/01/2023]
Abstract
Patients with congenital heart disease (CHD) requiring cardiac surgery in infancy are at high risk for neurodevelopmental impairments. Neonatal imaging studies have reported disruptions of brain functional organization before surgery. Yet, the extent to which functional network alterations are present after cardiac repair remains unexplored. This preliminary study aimed at investigating cortical functional connectivity in 4-month-old infants with repaired CHD, using resting-state functional near-infrared spectroscopy (fNIRS). After fNIRS signal frequency decomposition, we compared values of magnitude-squared coherence as a measure of connectivity strength, between 21 infants with corrected CHD and 31 healthy controls. We identified a subset of connections with differences between groups at an uncorrected statistical level of p < .05 while controlling for sex and maternal socioeconomic status, with most of these connections showing reduced connectivity in infants with CHD. Although none of these differences reach statistical significance after FDR correction, likely due to the small sample size, moderate to large effect sizes were found for group-differences. If replicated, these results would therefore suggest preliminary evidence that alterations of brain functional connectivity are present in the months after cardiac surgery. Additional studies involving larger sample size are needed to replicate our data, and comparisons between pre- and postoperative findings would allow to further delineate alterations of functional brain connectivity in this population.
Collapse
Affiliation(s)
- Solène Fourdain
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Sarah Provost
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Julie Tremblay
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | | | - Amélie Doussau
- Clinique d'investigation neurocardiaque (CINC), Sainte-Justine, Montreal University Hospital Center, Montreal, QC, Canada
| | - Laura Caron-Desrochers
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Isabelle Gaudet
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Kassandra Roger
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Alejandra Hüsser
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| | - Mathieu Dehaes
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
- Department of Radiology, Radio-oncology and Nuclear Medicine, Université de Montréal, Montreal, QC, Canada
| | | | - Nancy Poirier
- Clinique d'investigation neurocardiaque (CINC), Sainte-Justine, Montreal University Hospital Center, Montreal, QC, Canada
- Department of Surgery, Faculty of Medicine, Université de Montreal, Montreal, QC, Canada
| | - Anne Gallagher
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
- Sainte-Justine University Hospital Research Center, Montreal, QC, Canada
| |
Collapse
|
6
|
Opitz L, Wagner F, Rogenz J, Maas J, Schmidt A, Brodoehl S, Klingner CM. Still Wanting to Win: Reward System Stability in Healthy Aging. Front Aging Neurosci 2022; 14:863580. [PMID: 35707701 PMCID: PMC9190761 DOI: 10.3389/fnagi.2022.863580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
Healthy aging is accompanied by multi-faceted changes. Especially within the brain, healthy aging exerts substantial impetus on core parts of cognitive and motivational networks. Rewards comprise basic needs, such as food, sleep, and social contact. Thus, a functionally intact reward system remains indispensable for elderly people to cope with everyday life and adapt to their changing environment. Research shows that reward system function is better preserved in the elderly than most cognitive functions. To investigate the compensatory mechanisms providing reward system stability in aging, we employed a well-established reward paradigm (Monetary Incentive Delay Task) in groups of young and old participants while undergoing EEG measurement. As a new approach, we applied EEG connectivity analyses to assess cortical reward-related network connectivity. At the behavioral level, our results confirm that the function of the reward system is preserved in old age. The mechanisms identified for maintaining reward system function in old age do not fit into previously described models of cognitive aging. Overall, older adults exhibit lower reward-related connectivity modulation, higher reliance on posterior and right-lateralized brain areas than younger adults, and connectivity modulation in the opposite direction than younger adults, with usually greater connectivity during non-reward compared to reward conditions. We believe that the reward system has unique compensatory mechanisms distinct from other cognitive functions, probably due to its etymologically very early origin. In summary, this study provides important new insights into cortical reward network connectivity in healthy aging.
Collapse
Affiliation(s)
- Laura Opitz
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Franziska Wagner
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
- Clinician Scientist Program OrganAge, Jena University Hospital, Jena, Germany
- *Correspondence: Franziska Wagner,
| | - Jenny Rogenz
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Johanna Maas
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Alexander Schmidt
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Stefan Brodoehl
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| | - Carsten M. Klingner
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
- Biomagnetic Center, Jena University Hospital, Jena, Germany
| |
Collapse
|
7
|
LIONirs: flexible Matlab toolbox for fNIRS data analysis. J Neurosci Methods 2022; 370:109487. [DOI: 10.1016/j.jneumeth.2022.109487] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 11/21/2022]
|
8
|
Stancin I, Cifrek M, Jovic A. A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems. SENSORS 2021; 21:s21113786. [PMID: 34070732 PMCID: PMC8198610 DOI: 10.3390/s21113786] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 01/05/2023]
Abstract
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.
Collapse
|
9
|
Koberda JL. QEEG as a Useful Tool for the Evaluation of Early Cognitive Changes in Dementia and Traumatic Brain Injury. Clin EEG Neurosci 2021; 52:119-125. [PMID: 32391721 DOI: 10.1177/1550059420914816] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Quantitative electroencephalography (QEEG)-electrical neuroimaging has been underutilized in general neurology practice. Recent advances in computer technology have made this electrophysiological testing relatively inexpensive as well as precise in identifying brain areas with electrical dysfunction related to either traumatic injury or neurodegenerative process. In this article, the author presents 2 cases that can be frequently encountered in every general neurological practice: case of early dementia and traumatic brain injury. The clinical usefulness of QEEG is demonstrated by showing evidence of electrical abnormalities and networks dysfunctions (including an elevation of frontal/temporal delta and theta powers as well as abnormalities in functional connectivity). In addition, the correlation of QEEG and findings from structural imaging technique-magnetic resonance imaging diffusion tensor imaging and another functional imaging-positron emission tomography is presented.
Collapse
Affiliation(s)
- J Lucas Koberda
- Neurology, PL/Brain Enhancement Inc, TNBC, Tallahassee, FL, USA
| |
Collapse
|
10
|
On the Variability of Functional Connectivity and Network Measures in Source-Reconstructed EEG Time-Series. ENTROPY 2020; 23:e23010005. [PMID: 33375007 PMCID: PMC7822028 DOI: 10.3390/e23010005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/17/2020] [Accepted: 12/21/2020] [Indexed: 12/19/2022]
Abstract
The idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea has developed beyond its initial stages, its practical application is still far away from being widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying statistical interdependence among the (interacting) units. This issue has probably contributed to hindering comparisons among different studies. Not only do all these approaches go under the same name (functional connectivity), but they have often been tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often arbitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects the ambiguity and concern that should always be discussed when reporting findings based on any connectivity metric.
Collapse
|
11
|
de Camp NV, Ladwig-Wiegard M, Geitner CIE, Bergeler J, Thöne-Reineke C. EEG based assessment of stress in horses: a pilot study. PeerJ 2020; 8:e8629. [PMID: 32435527 PMCID: PMC7227666 DOI: 10.7717/peerj.8629] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 01/24/2020] [Indexed: 11/20/2022] Open
Abstract
As has been hypothesized more than 20 years ago, data derived from Electroencephalography (EEG) measurements can be used to distinguish between behavioral states associated with animal welfare. In our current study we found a high degree of correlation between the modulation index of phase related amplitude changes in the EEG of horses (n = 6 measurements with three different horses, mare and gelding) and their facial expression, measured by the use of the horse grimace scale. Furthermore, the pattern of phase amplitude coupling (PAC) was significantly different between a rest condition and a stress condition in horses. This pilot study paves the way for a possible use of EEG derived PAC as an objective tool for the assessment of animal welfare. Beyond that, the method might be useful to assess welfare aspects in the clinical setting for human patients, as for example in the neonatal intensive care unit.
Collapse
Affiliation(s)
- Nora V de Camp
- Behavioral Physiology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Freie Universität Berlin, Berlin, Germany
| | - Mechthild Ladwig-Wiegard
- Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Freie Universität Berlin, Berlin, Germany
| | - Carola I E Geitner
- Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Freie Universität Berlin, Berlin, Germany
| | - Jürgen Bergeler
- Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Freie Universität Berlin, Berlin, Germany.,Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christa Thöne-Reineke
- Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Freie Universität Berlin, Berlin, Germany
| |
Collapse
|
12
|
Gaudet I, Hüsser A, Vannasing P, Gallagher A. Functional Brain Connectivity of Language Functions in Children Revealed by EEG and MEG: A Systematic Review. Front Hum Neurosci 2020; 14:62. [PMID: 32226367 PMCID: PMC7080982 DOI: 10.3389/fnhum.2020.00062] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 02/10/2020] [Indexed: 01/29/2023] Open
Abstract
The development of language functions is of great interest to neuroscientists, as these functions are among the fundamental capacities of human cognition. For many years, researchers aimed at identifying cerebral correlates of language abilities. More recently, the development of new data analysis tools has generated a shift toward the investigation of complex cerebral networks. In 2015, Weiss-Croft and Baldeweg published a very interesting systematic review on the development of functional language networks, explored through the use of functional magnetic resonance imaging (fMRI). Compared to fMRI and because of their excellent temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) provide different and important information on brain activity. Both therefore constitute crucial neuroimaging techniques for the investigation of the maturation of functional language brain networks. The main objective of this systematic review is to provide a state of knowledge on the investigation of language-related cerebral networks in children, through the use of EEG and MEG, as well as a detailed portrait of relevant MEG and EEG data analysis methods used in that specific research context. To do so, we have summarized the results and systematically compared the methodological approach of 24 peer-reviewed EEG or MEG scientific studies that included healthy children and children with or at high risk of language disabilities, from birth up to 18 years of age. All included studies employed functional and effective connectivity measures, such as coherence, phase locking value, and Phase Slope Index, and did so using different experimental paradigms (e.g., at rest or during language-related tasks). This review will provide more insight into the use of EEG and MEG for the study of language networks in children, contribute to the current state of knowledge on the developmental path of functional connectivity in language networks during childhood and adolescence, and finally allow future studies to choose the most appropriate type of connectivity analysis.
Collapse
Affiliation(s)
- Isabelle Gaudet
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada.,Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Alejandra Hüsser
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada.,Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Phetsamone Vannasing
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Anne Gallagher
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada.,Department of Psychology, Université de Montréal, Montréal, QC, Canada
| |
Collapse
|
13
|
Demuru M, La Cava SM, Pani SM, Fraschini M. A comparison between power spectral density and network metrics: An EEG study. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101760] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
14
|
Dickinson A, Varcin KJ, Sahin M, Nelson CA, Jeste SS. Early patterns of functional brain development associated with autism spectrum disorder in tuberous sclerosis complex. Autism Res 2019; 12:1758-1773. [PMID: 31419043 PMCID: PMC6898751 DOI: 10.1002/aur.2193] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 07/16/2019] [Accepted: 07/19/2019] [Indexed: 01/12/2023]
Abstract
Tuberous sclerosis complex (TSC) is a rare genetic disorder that confers a high risk for autism spectrum disorders (ASD), with behavioral predictors of ASD emerging early in life. Deviations in structural and functional neural connectivity are highly implicated in both TSC and ASD. For the first time, we explore whether electroencephalographic (EEG) measures of neural network function precede or predict the emergence of ASD in TSC. We determine whether altered brain function (a) is present in infancy in TSC, (b) differentiates infants with TSC based on ASD diagnostic status, and (c) is associated with later cognitive function. We studied 35 infants with TSC (N = 35), and a group of typically developing infants (N = 20) at 12 and 24 months of age. Infants with TSC were later subdivided into ASD and non-ASD groups based on clinical evaluation. We measured features of spontaneous alpha oscillations (6-12 Hz) that are closely associated with neural network development: alpha power, alpha phase coherence (APC), and peak alpha frequency (PAF). Infants with TSC demonstrated reduced interhemispheric APC compared to controls at 12 months of age, and these differences were found to be most pronounced at 24 months in the infants who later developed ASD. Across all infants, PAF at 24 months was associated with verbal and nonverbal cognition at 36 months. Associations between early network function and later neurodevelopmental and cognitive outcomes highlight the potential utility of early scalable EEG markers to identify infants with TSC requiring additional targeted intervention initiated very early in life. Autism Res 2019, 12: 1758-1773. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Approximately half of infants with tuberous sclerosis complex (TSC) develop autism. Here, using EEG, we find that there is a reduction in communication between brain regions during infancy in TSC, and that the infants who show the largest reductions are those who later develop autism. Being able to identify infants who show early signs of disrupted brain development may improve the timing of early prediction and interventions in TSC, and also help us to understand how early brain changes lead to autism.
Collapse
Affiliation(s)
- Abigail Dickinson
- UCLA Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, Los Angeles, California
| | - Kandice J Varcin
- Telethon Kids Institute, University of Western Australia, Subiaco, Western Australia, Australia
| | - Mustafa Sahin
- Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
- Harvard Graduate School of Education, Cambridge, Massachusetts
| | - Shafali S Jeste
- UCLA Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, Los Angeles, California
| |
Collapse
|
15
|
Zweiphenning WJEM, Keijzer HM, van Diessen E, van ‘t Klooster MA, van Klink NEC, Leijten FSS, van Rijen PC, van Putten MJAM, Braun KPJ, Zijlmans M. Increased gamma and decreased fast ripple connections of epileptic tissue: A high-frequency directed network approach. Epilepsia 2019; 60:1908-1920. [PMID: 31329277 PMCID: PMC6852371 DOI: 10.1111/epi.16296] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 01/11/2023]
Abstract
OBJECTIVE New insights into high-frequency electroencephalographic activity and network analysis provide potential tools to improve delineation of epileptic tissue and increase the chance of postoperative seizure freedom. Based on our observation of high-frequency oscillations "spreading outward" from the epileptic source, we hypothesize that measures of directed connectivity in the high-frequency range distinguish epileptic from healthy brain tissue. METHODS We retrospectively selected refractory epilepsy patients with a malformation of cortical development or tumor World Health Organization grade I/II who underwent epilepsy surgery with intraoperative electrocorticography for tailoring the resection based on spikes. We assessed directed functional connectivity in the theta (4-8 Hz), gamma (30-80 Hz), ripple (80-250 Hz), and fast ripple (FR; 250-500 Hz) bands using the short-time direct directed transfer function, and calculated the total, incoming, and outgoing propagation strength for each electrode. We compared network measures of electrodes covering the resected and nonresected areas separately for patients with good and poor outcome, and of electrodes with and without spikes, ripples, and FRs (group level: paired t test; patient level: Mann-Whitney U test). We selected the measure that could best identify the resected area and channels with epileptic events using the area under the receiver operating characteristic curve, and calculated the positive and negative predictive value, sensitivity, and specificity. RESULTS We found higher total and outstrength in the ripple and gamma bands in resected tissue in patients with good outcome (rippletotal : P = .01; rippleout : P = .04; gammatotal : P = .01; gammaout : P = .01). Channels with events showed lower total and instrength, and higher outstrength in the FR band, and higher total and outstrength in the ripple, gamma, and theta bands (FRtotal : P = .05; FRin : P < .01; FRout : P = .02; gammatotal : P < .01; gammain : P = .01; gammaout : P < .01; thetatotal : P = .01; thetaout : P = .01). The total strength in the gamma band was most distinctive at the channel level (positive predictive value [PPV]good = 74%, PPVpoor = 43%). SIGNIFICANCE Interictally, epileptic tissue is isolated in the FR band and acts as a driver up to the (fast) ripple frequency range. The gamma band total strength seems promising to delineate epileptic tissue intraoperatively.
Collapse
Affiliation(s)
- Willemiek J. E. M. Zweiphenning
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Hanneke M. Keijzer
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
- MIRA Institute for Biomedical Technology and Technical MedicineClinical Neurophysiology GroupUniversity of TwenteEnschedethe Netherlands
| | - Eric van Diessen
- Department of Pediatric NeurologyUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Maryse A. van ‘t Klooster
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Nicole E. C. van Klink
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Frans S. S. Leijten
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Peter C. van Rijen
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Michel J. A. M. van Putten
- MIRA Institute for Biomedical Technology and Technical MedicineClinical Neurophysiology GroupUniversity of TwenteEnschedethe Netherlands
| | - Kees P. J. Braun
- Department of Pediatric NeurologyUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Maeike Zijlmans
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
- Epilepsy Foundation of the NetherlandsHeemstedethe Netherlands
| |
Collapse
|
16
|
Fraschini M, Pani SM, Didaci L, Marcialis GL. Robustness of functional connectivity metrics for EEG-based personal identification over task-induced intra-class and inter-class variations. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.03.025] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
17
|
Tremblay S, Rogasch NC, Premoli I, Blumberger DM, Casarotto S, Chen R, Di Lazzaro V, Farzan F, Ferrarelli F, Fitzgerald PB, Hui J, Ilmoniemi RJ, Kimiskidis VK, Kugiumtzis D, Lioumis P, Pascual-Leone A, Pellicciari MC, Rajji T, Thut G, Zomorrodi R, Ziemann U, Daskalakis ZJ. Clinical utility and prospective of TMS–EEG. Clin Neurophysiol 2019; 130:802-844. [DOI: 10.1016/j.clinph.2019.01.001] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 01/07/2019] [Accepted: 01/08/2019] [Indexed: 12/15/2022]
|
18
|
Tozzi A. The multidimensional brain. Phys Life Rev 2019; 31:86-103. [PMID: 30661792 DOI: 10.1016/j.plrev.2018.12.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Revised: 05/17/2018] [Accepted: 12/27/2018] [Indexed: 01/24/2023]
Abstract
Brain activity takes place in three spatial-plus time dimensions. This rather obvious claim has been recently questioned by papers that, taking into account the big data outburst and novel available computational tools, are starting to unveil a more intricate state of affairs. Indeed, various brain activities and their correlated mental functions can be assessed in terms of trajectories embedded in phase spaces of dimensions higher than the canonical ones. In this review, I show how further dimensions may not just represent a convenient methodological tool that allows a better mathematical treatment of otherwise elusive cortical activities, but may also reflect genuine functional or anatomical relationships among real nervous functions. I then describe how to extract hidden multidimensional information from real or artificial neurodata series, and make clear how our mind dilutes, rather than concentrates as currently believed, inputs coming from the environment. Finally, I argue that the principle "the higher the dimension, the greater the information" may explain the occurrence of mental activities and elucidate the mechanisms of human diseases associated with dimensionality reduction.
Collapse
Affiliation(s)
- Arturo Tozzi
- Center for Nonlinear Science, University of North Texas, 1155 Union Circle, #311427 Denton, TX 76203-5017, USA.
| |
Collapse
|
19
|
Dickinson A, DiStefano C, Lin YY, Scheffler AW, Senturk D, Jeste SS. Interhemispheric alpha-band hypoconnectivity in children with autism spectrum disorder. Behav Brain Res 2018; 348:227-234. [PMID: 29689375 PMCID: PMC5993636 DOI: 10.1016/j.bbr.2018.04.026] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 04/12/2018] [Accepted: 04/17/2018] [Indexed: 12/16/2022]
Abstract
Diverse genetic and environmental etiologies converge onto circuit level brain dysfunction in autism spectrum disorder (ASD), manifesting at a macroscopic level as aberrant neural connectivity. Previous studies have described atypical patterns of decreased short range and increased long range connectivity in ASD [1 ]. However, it remains unclear whether group level features of circuit dysfunction are consistently present across the range of cognitive function seen in the autism spectrum. The dynamics of neural oscillations in the alpha range (6-12 Hz) are exquisitely sensitive to healthy development of functional and structural connectivity. Alpha-band coherence, measured with high temporal-precision electroencephalography (EEG) therefore represents an ideal tool for studying neural connectivity in developmental populations. Here we examined spontaneous alpha phase coherence in a heterogeneous sample of 59 children with ASD and 39 age matched typically developing children. Using a data driven approach, we conducted an unbiased examination of all possible atypical connectivity patterns across all cortical regions. Long-range hypoconnectivity was present in children with ASD compared to typically developing children, with temporal interhemispheric connectivity showing the largest difference between the two groups. Decreased long range alpha coherence distinguishes a heterogeneous group of ASD children from typically developing children. Interhemispheric temporal hypoconnectivity represents a fundamental functional difference in children with ASD across a wide cognitive and age range that may reflect white matter disturbances or increased signal variability at temporal sites in ASD.
Collapse
Affiliation(s)
- Abigail Dickinson
- Center for Autism Research and Treatment, University of California, Semel Institute for Neuroscience, 760 Westwood Plaza, Suite A7-452 Los Angeles, CA, 90095, United States.
| | - Charlotte DiStefano
- Center for Autism Research and Treatment, University of California, Semel Institute for Neuroscience, 760 Westwood Plaza, Suite A7-452 Los Angeles, CA, 90095, United States
| | - Yin-Ying Lin
- Center for Autism Research and Treatment, University of California, Semel Institute for Neuroscience, 760 Westwood Plaza, Suite A7-452 Los Angeles, CA, 90095, United States
| | - Aaron Wolfe Scheffler
- Department of Biostatistics, UCLA School of Public Health, Room 21-254C, CHS, Los Angeles, CA, 90095, United States
| | - Damla Senturk
- Department of Biostatistics, UCLA School of Public Health, Room 21-254C, CHS, Los Angeles, CA, 90095, United States
| | - Shafali Spurling Jeste
- Center for Autism Research and Treatment, University of California, Semel Institute for Neuroscience, 760 Westwood Plaza, Suite A7-452 Los Angeles, CA, 90095, United States
| |
Collapse
|
20
|
Syntax meets semantics during brain logical computations. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 140:133-141. [PMID: 29803722 DOI: 10.1016/j.pbiomolbio.2018.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 04/26/2018] [Accepted: 05/23/2018] [Indexed: 01/20/2023]
Abstract
The discrepancy between syntax and semantics is a painstaking issue that hinders a better comprehension of the underlying neuronal processes in the human brain. In order to tackle the issue, we at first describe a striking correlation between Wittgenstein's Tractatus, that assesses the syntactic relationships between language and world, and Perlovsky's joint language-cognitive computational model, that assesses the semantic relationships between emotions and "knowledge instinct". Once established a correlation between a purely logical approach to the language and computable psychological activities, we aim to find the neural correlates of syntax and semantics in the human brain. Starting from topological arguments, we suggest that the semantic properties of a proposition are processed in higher brain's functional dimensions than the syntactic ones. In a fully reversible process, the syntactic elements embedded in Broca's area project into multiple scattered semantic cortical zones. The presence of higher functional dimensions gives rise to the increase in informational content that takes place in semantic expressions. Therefore, diverse features of human language and cognitive world can be assessed in terms of both the logic armor described by the Tractatus, and the neurocomputational techniques at hand. One of our motivations is to build a neuro-computational framework able to provide a feasible explanation for brain's semantic processing, in preparation for novel computers with nodes built into higher dimensions.
Collapse
|
21
|
Fraschini M, Lai M, Demuru M, Puligheddu M, Floris G, Borghero G, Marrosu F. Functional brain connectivity analysis in amyotrophic lateral sclerosis: an EEG source-space study. Biomed Phys Eng Express 2018. [DOI: 10.1088/2057-1976/aa9c64] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
22
|
Tozzi A, Peters JF. What is it like to be “the same”? PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 133:30-35. [DOI: 10.1016/j.pbiomolbio.2017.10.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 10/24/2017] [Accepted: 10/26/2017] [Indexed: 01/05/2023]
|
23
|
Developmental trends of theta-beta interelectrode power correlation during resting state in normal children. Cogn Neurodyn 2018; 12:255-269. [PMID: 29765476 DOI: 10.1007/s11571-018-9476-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 12/16/2017] [Accepted: 01/17/2018] [Indexed: 10/18/2022] Open
Abstract
The possibility that power-to-power (theta-beta) frequency coupling increases during development was analyzed. Three minutes of spontaneous EEG in an open eyes condition were recorded in a sample of 160 subjects ranging from 6 to 26 years old. Theta (4-7 Hz) and beta band (15-20 Hz) power was calculated in a trial-by-trial basis. Inter-electrode power correlations (IPC) were computed in each subject as the correlation between the power of two frequency bands recorded in two electrodes. An increase in theta-beta IPC with age was obtained. IPCs were higher when theta was seeded in posterior regions than in anterior or central regions. Moreover, the significant correlations between each individual IPC and age were calculated, making it possible to draw IPC versus age correlation maps in order to capture the IPC development topography. An increase was found in significant correlations in the left hemisphere compared to the right hemisphere. There were no differences in the inter-hemispheric versus intra-hemispheric IPC maturation spatial patterns. An increase in power-to-power-frequency coupling in theta-beta occurs during development, suggesting an increase in functional connectivity with age. Frequency coupling between theta and beta rhythms would be one of the mechanisms facilitating integration of long distance functional networks during development.
Collapse
|
24
|
Mellem MS, Wohltjen S, Gotts SJ, Ghuman AS, Martin A. Intrinsic frequency biases and profiles across human cortex. J Neurophysiol 2017; 118:2853-2864. [PMID: 28835521 DOI: 10.1152/jn.00061.2017] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 08/09/2017] [Accepted: 08/22/2017] [Indexed: 11/22/2022] Open
Abstract
Recent findings in monkeys suggest that intrinsic periodic spiking activity in selective cortical areas occurs at timescales that follow a sensory or lower order-to-higher order processing hierarchy (Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Padoa-Schioppa C, Pasternak T, Seo H, Lee D, Wang XJ. Nat Neurosci 17: 1661-1663, 2014). It has not yet been fully explored if a similar timescale hierarchy is present in humans. Additionally, these measures in the monkey studies have not addressed findings that rhythmic activity within a brain area can occur at multiple frequencies. In this study we investigate in humans if regions may be biased toward particular frequencies of intrinsic activity and if a full cortical mapping still reveals an organization that follows this hierarchy. We examined the spectral power in multiple frequency bands (0.5-150 Hz) from task-independent data using magnetoencephalography (MEG). We compared standardized power across bands to find regional frequency biases. Our results demonstrate a mix of lower and higher frequency biases across sensory and higher order regions. Thus they suggest a more complex cortical organization that does not simply follow this hierarchy. Additionally, some regions do not display a bias for a single band, and a data-driven clustering analysis reveals a regional organization with high standardized power in multiple bands. Specifically, theta and beta are both high in dorsal frontal cortex, whereas delta and gamma are high in ventral frontal cortex and temporal cortex. Occipital and parietal regions are biased more narrowly toward alpha power, and ventral temporal lobe displays specific biases toward gamma. Thus intrinsic rhythmic neural activity displays a regional organization but one that is not necessarily hierarchical.NEW & NOTEWORTHY The organization of rhythmic neural activity is not well understood. Whereas it has been postulated that rhythms are organized in a hierarchical manner across brain regions, our novel analysis allows comparison of full cortical maps across different frequency bands, which demonstrate that the rhythmic organization is more complex. Additionally, data-driven methods show that rhythms of multiple frequencies or timescales occur within a particular region and that this nonhierarchical organization is widespread.
Collapse
Affiliation(s)
- Monika S Mellem
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland; and
| | - Sophie Wohltjen
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland; and
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland; and
| | - Avniel Singh Ghuman
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland; and.,Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland; and
| |
Collapse
|
25
|
Wang MY, Wang J, Zhou J, Guan YG, Zhai F, Liu CQ, Xu FF, Han YX, Yan ZF, Luan GM. Identification of the epileptogenic zone of temporal lobe epilepsy from stereo-electroencephalography signals: A phase transfer entropy and graph theory approach. NEUROIMAGE-CLINICAL 2017; 16:184-195. [PMID: 28794979 PMCID: PMC5542420 DOI: 10.1016/j.nicl.2017.07.022] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 06/15/2017] [Accepted: 07/22/2017] [Indexed: 01/09/2023]
Abstract
The aim of this research is to apply an approach based on phase transfer entropy (PTE) and graph theory to study the interactions between the stereo-electroencephalography (SEEG) activities recorded in multilobar origin, in order to evaluate their ability to detect the epileptogenic zone (EZ) of temporal lobe epilepsies (TLE). Forty-three patients were included in this retrospective study. Five to sixteen (median = 12) multilead electrodes were implanted per patient, and, for each patient, a sub-set of between 10 and 32 (median = 22) bipolar derivations was selected for analysis. The leads were classified into the onset leads (OLs), the early propagation leads (EPLs), and the rest of the leads (RLs). The results showed that a significantly different dynamic trend of the out/in ratio (more obvious in the gamma band) distinguishes the OLs from RLs in the 23 patients who were seizure-free not only during the ictal event (significant elevation), but also during the inter-,pre-, late-ictal periods, and especially in the post-ictal (sharp decline) state. However, in the 20 patients who were not-seizure-free, the differences between the OLs and RLs during the post-ictal period were not found in any frequency band. The dynamic trend was used to predict surgical outcome, and the results showed that the sensitivity was 91% and the specificity was 70%. In brief, this study indicates that our approach may add new and valuable information, providing efficient quantitative measures useful for localizing the EZ.
Collapse
Affiliation(s)
- Meng-Yang Wang
- Epilepsy Center and Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Jing Wang
- Epilepsy Center and Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Jian Zhou
- Epilepsy Center and Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Yu-Guang Guan
- Epilepsy Center and Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Feng Zhai
- Epilepsy Center and Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Chang-Qing Liu
- Epilepsy Center and Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Fei-Fei Xu
- Epilepsy Center and Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Yi-Xian Han
- Epilepsy Center and Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Zhao-Fen Yan
- Epilepsy Center and Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| | - Guo-Ming Luan
- Epilepsy Center and Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing Key Laboratory of Epilepsy, Beijing Institute for Brain Disorders, 50, Xiang-shan-yi-ke-song, Haidian District, Beijing 100093, China
| |
Collapse
|
26
|
Tozzi A, Peters JF. From abstract topology to real thermodynamic brain activity. Cogn Neurodyn 2017; 11:283-292. [PMID: 28559956 PMCID: PMC5430247 DOI: 10.1007/s11571-017-9431-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 02/14/2017] [Accepted: 03/08/2017] [Indexed: 12/25/2022] Open
Abstract
Recent approaches to brain phase spaces reinforce the foremost role of symmetries and energy requirements in the assessment of nervous activity. Changes in thermodynamic parameters and dimensions occur in the brain during symmetry breakings and transitions from one functional state to another. Based on topological results and string-like trajectories into nervous energy landscapes, we provide a novel method for the evaluation of energetic features and constraints in different brain functional activities. We show how abstract approaches, namely the Borsuk-Ulam theorem and its variants, may display real, energetic physical counterparts. When topology meets the physics of the brain, we arrive at a general model of neuronal activity, in terms of multidimensional manifolds and computational geometry, that has the potential to be operationalized.
Collapse
Affiliation(s)
- Arturo Tozzi
- Center for Nonlinear Science, Department of Physics, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017 USA
| | - James F. Peters
- Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor’s Circle, Winnipeg, MB R3T 5V6 Canada
| |
Collapse
|
27
|
Engels MMA, van der Flier WM, Stam CJ, Hillebrand A, Scheltens P, van Straaten ECW. Alzheimer's disease: The state of the art in resting-state magnetoencephalography. Clin Neurophysiol 2017. [PMID: 28622527 DOI: 10.1016/j.clinph.2017.05.012] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Alzheimer's disease (AD) is accompanied by functional brain changes that can be detected in imaging studies, including electromagnetic activity recorded with magnetoencephalography (MEG). Here, we systematically review the studies that have examined resting-state MEG changes in AD and identify areas that lack scientific or clinical progress. Three levels of MEG analysis will be covered: (i) single-channel signal analysis, (ii) pairwise analyses over time series, which includes the study of interdependencies between two time series and (iii) global network analyses. We discuss the findings in the light of other functional modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Overall, single-channel MEG results show consistent changes in AD that are in line with EEG studies, but the full potential of the high spatial resolution of MEG and advanced functional connectivity and network analysis has yet to be fully exploited. Adding these features to the current knowledge will potentially aid in uncovering organizational patterns of brain function in AD and thereby aid the understanding of neuronal mechanisms leading to cognitive deficits.
Collapse
Affiliation(s)
- M M A Engels
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
| | - W M van der Flier
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Epidemiology and Biostatistics, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Ph Scheltens
- Alzheimer Centrum and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - E C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| |
Collapse
|
28
|
Peters JF, Ramanna S, Tozzi A, İnan E. Bold-Independent Computational Entropy Assesses Functional Donut-Like Structures in Brain fMRI Images. Front Hum Neurosci 2017; 11:38. [PMID: 28203153 PMCID: PMC5285359 DOI: 10.3389/fnhum.2017.00038] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2016] [Accepted: 01/18/2017] [Indexed: 11/13/2022] Open
Abstract
We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. This approach befits the geometric character of fMRIs. The main purpose of this study is to provide a computable framework for fMRI that not only facilitates analyses, but also provides an easily decipherable visualization of structures. This framework commands attention because it is easily implemented using conventional software systems. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimension's distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Rényi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain.
Collapse
Affiliation(s)
- James F Peters
- Department of Electrical and Computer Engineering, University of ManitobaWinnipeg, MB, Canada; Department of Mathematics, Adıyaman UniversityAdıyaman, Turkey; Department of Mathematics, Faculty of Arts and Sciences, Adıyaman UniversityAdıyaman, Turkey; Computational Intelligence Laboratory, University of ManitobaWinnipeg, MB, Canada
| | - Sheela Ramanna
- Department of Applied Computer Science, University of Winnipeg Winnipeg, MB, Canada
| | - Arturo Tozzi
- Department of Physics, Center for Nonlinear Science, University of North Texas Denton, TX, USA
| | - Ebubekir İnan
- Department of Mathematics, Faculty of Arts and Sciences, Adıyaman UniversityAdıyaman, Turkey; Computational Intelligence Laboratory, University of ManitobaWinnipeg, MB, Canada
| |
Collapse
|
29
|
Towards Topological Mechanisms Underlying Experience Acquisition and Transmission in the Human Brain. Integr Psychol Behav Sci 2017; 51:303-323. [DOI: 10.1007/s12124-017-9380-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
|
30
|
|
31
|
Thatcher RW, Palmero-Soler E, North DM, Biver CJ. Intelligence and eeg measures of information flow: efficiency and homeostatic neuroplasticity. Sci Rep 2016; 6:38890. [PMID: 27996049 PMCID: PMC5171906 DOI: 10.1038/srep38890] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 11/14/2016] [Indexed: 12/22/2022] Open
Abstract
The purpose of this study was to explore the relationship between the magnitude of EEG information flow and intelligence. The electroencephalogram (EEG) was recorded from 19 scalp locations from 371 subjects ranging in age from 5 years to 17.6 years. The Wechler Intelligence Scale for Children (WISC-R) was administered for individuals between 5 years of age and 16 years and the Weschler Adult Intelligence Scale revised (WAIS-R) was administered to subjects older than 16 years to estimate I.Q. The phase slope index estimated the magnitude of information flow between all electrode combinations for difference frequency bands. Discriminant analyses were performed between high I.Q. (>120) and low I.Q. groups (<90). The magnitude of information flow was inversely related to I.Q. especially in the alpha and beta frequency bands. Long distance inter-electrode distances exhibited greater information flow than short inter-electrode distances. Frontal-parietal correlations were the most significant. It is concluded that higher I.Q. is related to increased efficiency of local information processing and reduced long distance compensatory dynamics that supports a small-world model of intelligence.
Collapse
Affiliation(s)
- R W Thatcher
- EEG and NeuroImaging Laboratory, Applied Neuroscience Research Institute. St. Petersburg, Fl, USA
| | - E Palmero-Soler
- EEG and NeuroImaging Laboratory, Applied Neuroscience Research Institute. St. Petersburg, Fl, USA
| | - D M North
- EEG and NeuroImaging Laboratory, Applied Neuroscience Research Institute. St. Petersburg, Fl, USA
| | - C J Biver
- EEG and NeuroImaging Laboratory, Applied Neuroscience Research Institute. St. Petersburg, Fl, USA
| |
Collapse
|
32
|
Zweiphenning W, van ‘t Klooster M, van Diessen E, van Klink N, Huiskamp G, Gebbink T, Leijten F, Gosselaar P, Otte W, Stam C, Braun K, Zijlmans G. High frequency oscillations and high frequency functional network characteristics in the intraoperative electrocorticogram in epilepsy. Neuroimage Clin 2016; 12:928-939. [PMID: 27882298 PMCID: PMC5114532 DOI: 10.1016/j.nicl.2016.09.014] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 08/29/2016] [Accepted: 09/21/2016] [Indexed: 11/21/2022]
Abstract
OBJECTIVE High frequency oscillations (HFOs; > 80 Hz), especially fast ripples (FRs, 250-500 Hz), are novel biomarkers for epileptogenic tissue. The pathophysiology suggests enhanced functional connectivity within FR generating tissue. Our aim was to determine the relation between brain areas showing FRs and 'baseline' functional connectivity within EEG networks, especially in the high frequency bands. METHODS We marked FRs, ripples (80-250 Hz) and spikes in the electrocorticogram of 14 patients with refractory temporal lobe epilepsy. We assessed 'baseline' functional connectivity in epochs free of epileptiform events within these recordings, using the phase lag index. We computed the Eigenvector Centrality (EC) per channel in the FR and gamma band network. We compared EC between channels that did or did not show events at other moments in time. RESULTS FR-band EC was higher in channels with than without spikes. Gamma-band EC was lower in channels with ripples and FRs. CONCLUSIONS We confirmed previous findings of functional isolation in the gamma-band and found a first proof of functional integration in the FR-band network of channels covering presumed epileptogenic tissue. SIGNIFICANCE 'Baseline' high-frequency network parameters might help intra-operative recognition of epileptogenic tissue without the need for waiting for events. These findings can increase our understanding of the 'architecture' of epileptogenic networks and help unravel the pathophysiology of HFOs.
Collapse
Key Words
- (io)ECoG, (intra-operative) electrocorticography
- EC, eigenvector centrality
- EEG, electroencephalography
- Epilepsy
- Epilepsy surgery
- Epileptogenic zone
- FR, fast ripple, 250–500 Hz
- Functional network analysis
- HFO, high frequency oscillation, > 80 Hz
- High Frequency Oscillations
- IPSP, inhibitory postsynaptic potential
- PLI, phase lag index
- SOZ, seizure onset zone
- TLE, temporal lobe epilepsy
Collapse
Affiliation(s)
- W.J.E.M. Zweiphenning
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - M.A. van ‘t Klooster
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - E. van Diessen
- Brain Center Rudolf Magnus, Department of Pediatric Neurology, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - N.E.C. van Klink
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - G.J.M. Huiskamp
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - T.A. Gebbink
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - F.S.S. Leijten
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - P.H. Gosselaar
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - W.M. Otte
- Brain Center Rudolf Magnus, Department of Pediatric Neurology, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
- Stichting Epilepsie Instellingen Nederland, Heemstede, P.O. box 540, 2130 AM Hoofddorp, The Netherlands
| | - C.J. Stam
- Department of Clinical Neurophysiology, Neuroscience Campus Amsterdam, VU University Medical Center, Postbus 7057, 1007 MB Amsterdam, The Netherlands
| | - K.P.J. Braun
- Brain Center Rudolf Magnus, Department of Pediatric Neurology, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
| | - G.J.M. Zijlmans
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, UMC Utrecht, P.O. box 85500, 3508 GA Utrecht, The Netherlands
- Stichting Epilepsie Instellingen Nederland, Heemstede, P.O. box 540, 2130 AM Hoofddorp, The Netherlands
| |
Collapse
|
33
|
Alhourani A, Wozny TA, Krishnaswamy D, Pathak S, Walls SA, Ghuman AS, Krieger DN, Okonkwo DO, Richardson RM, Niranjan A. Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury. J Neurophysiol 2016; 116:1840-1847. [PMID: 27466136 DOI: 10.1152/jn.00513.2016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 07/25/2016] [Indexed: 12/30/2022] Open
Abstract
Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects (n = 9) compared with age-matched control subjects (n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.
Collapse
Affiliation(s)
- Ahmad Alhourani
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Thomas A Wozny
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Deepa Krishnaswamy
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Sudhir Pathak
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Shawn A Walls
- University of Pittsburgh Medical Center Brain Mapping Center, Pittsburgh, Pennsylvania
| | - Avniel S Ghuman
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Center for the Neural Basis of Cognition and University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, Pennsylvania; and
| | - Donald N Krieger
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - David O Okonkwo
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Center for the Neural Basis of Cognition and University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, Pennsylvania; and
| | - Ajay Niranjan
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania;
| |
Collapse
|