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Pourmotabbed H, Clarke DF, Chang C, Babajani-Feremi A. Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology. Commun Biol 2024; 7:1221. [PMID: 39349968 PMCID: PMC11443053 DOI: 10.1038/s42003-024-06807-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 08/29/2024] [Indexed: 10/04/2024] Open
Abstract
Cognitive, behavioral, and disease traits are influenced by both genetic and environmental factors. Individual differences in these traits have been associated with graph theoretical properties of resting-state networks, indicating that variations in connectome topology may be driven by genetics. In this study, we establish the heritability of global and local graph properties of resting-state networks derived from functional MRI (fMRI) and magnetoencephalography (MEG) using a large sample of twins and non-twin siblings from the Human Connectome Project. We examine the heritability of MEG in the source space, providing a more accurate estimate of genetic influences on electrophysiological networks. Our findings show that most graph measures are more heritable for MEG compared to fMRI and the heritability for MEG is greater for amplitude compared to phase synchrony in the delta, high beta, and gamma frequency bands. This suggests that the fast neuronal dynamics in MEG offer unique insights into the genetic basis of brain network organization. Furthermore, we demonstrate that brain network features can serve as genetic fingerprints to accurately identify pairs of identical twins within a cohort. These results highlight novel opportunities to relate individual connectome signatures to genetic mechanisms underlying brain function.
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Affiliation(s)
- Haatef Pourmotabbed
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Dave F Clarke
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Abbas Babajani-Feremi
- Magnetoencephalography (MEG) Lab, The Norman Fixel Institute of Neurological Diseases, Gainesville, FL, USA.
- Department of Neurology, University of Florida, Gainesville, FL, USA.
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2
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Chizhikova AA. [Electroencephalography: features of the obtained data and its applicability in psychiatry]. Zh Nevrol Psikhiatr Im S S Korsakova 2024; 124:31-39. [PMID: 38884427 DOI: 10.17116/jnevro202412405131] [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] [Indexed: 06/18/2024]
Abstract
Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.
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Affiliation(s)
- A A Chizhikova
- Centre for Strategic Planning and Management of Biomedical Health Risks of the Federal Medical Biological Agency, Moscow, Russia
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3
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Sang F, Xu K, Chen Y. Brain Network Organization and Aging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1419:99-108. [PMID: 37418209 DOI: 10.1007/978-981-99-1627-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Despite recent substantial progress in neuroscience, the mechanisms and principles of the complex structure, functions, and the relationship between the brain and cognitive functions have not been fully understood. The modeling method of brain network can provide a new perspective for neuroscience research, and it is possible to provide new solutions to the related research problems. On this basis, the researchers define the concept of human brain connectome to highlight and emphasize the importance of network modeling methods in neuroscience. For example, using diffusion-weighted magnetic resonance imaging (dMRI) technology and fiber tractography methods, a white matter connection network of the whole brain can be constructed. From the perspective of brain function, functional magnetic resonance imaging (fMRI) data can build the brain functional connection network. A structural covariation modeling method is used to obtain a brain structure covariation network, and it appears to reflect developmental coordination or synchronized maturation between areas of the brain. In addition, network modeling and analysis methods can also be applied to other types of image data, such as positron emission tomography (PET), electroencephalogram (EEG), and magnetoencephalography (MEG). This chapter mainly reviews the research progress of researchers on brain structure, function, and other aspects at the network level in recent years.
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Affiliation(s)
- Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China.
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China.
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4
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Rios-Arismendy S, Ochoa-Gómez JF, Serna-Rojas C. Revisión de electroencefalografía portable y su aplicabilidad en neurociencias. REVISTA POLITÉCNICA 2021. [DOI: 10.33571/rpolitec.v17n34a9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
La electroencefalografía (EEG) es una técnica que permite registrar la actividad eléctrica del cerebro y ha sido estudiada durante los últimos cien años en diferentes ámbitos de la neurociencia. En los últimos años se ha investigado y desarrollado equipos de medición que sean portables y que permitan una buena calidad de la señal, por lo cual se realizó una revisión bibliográfica de las compañías fabricantes de algunos dispositivos de electroencefalografía portable disponibles en el mercado, se exponen sus características principales, algunos trabajos encontrados que fueron realizados con los dispositivos, comparaciones entre los mismos y una discusión acerca de las ventajas y desventajas de sus características. Finalmente se concluye que a la hora de comprar un dispositivo para electroencefalografía portable es necesario tener en cuenta el uso que se le va a dar y el costo-beneficio que tiene el equipo de acuerdo con sus características.
Encephalography is a technique that allows the recording of electrical activity of the brain and has been studied during the last hundred years in different areas of neuroscience. For several years, measuring equipment that are portable and that allow a good signal quality to have been researched and developed, so a literature review of the manufacturing companies of some of portable electroencephalography devices available on the market was carried out: Its main features are exposed, as well as some of the work found that were made with those, comparisons between them and a discussion about the advantages and disadvantages of their features. It is concluded that, when a portable encephalography device is bought, it’s necessary to take into consideration the use that it will be having and the cost-benefit that the device has according to its features.
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5
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Smit DJA, Andreassen OA, Boomsma DI, Burwell SJ, Chorlian DB, de Geus EJC, Elvsåshagen T, Gordon RL, Harper J, Hegerl U, Hensch T, Iacono WG, Jawinski P, Jönsson EG, Luykx JJ, Magne CL, Malone SM, Medland SE, Meyers JL, Moberget T, Porjesz B, Sander C, Sisodiya SM, Thompson PM, van Beijsterveldt CEM, van Dellen E, Via M, Wright MJ. Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity. Brain Behav 2021; 11:e02188. [PMID: 34291596 PMCID: PMC8413828 DOI: 10.1002/brb3.2188] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 03/12/2021] [Accepted: 04/30/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND AND PURPOSE The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. METHODS We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. RESULTS We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. CONCLUSION The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.
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Affiliation(s)
- Dirk J A Smit
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Scott J Burwell
- Department of Psychology, Minnesota Center for Twin and Family Research, University of Minnesota, Minneapolis, MN, USA.,Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - David B Chorlian
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry, Downstate Health Sciences University, Brooklyn, NY, USA
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Torbjørn Elvsåshagen
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Reyna L Gordon
- Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Jeremy Harper
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | - Ulrich Hegerl
- Department of Psychiatry, Psychosomatics, and Psychotherapy, Goethe Universität Frankfurt am Main, Frankfurt, Germany
| | - Tilman Hensch
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany.,LIFE - Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany.,IU International University, Erfurt, Germany
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Philippe Jawinski
- LIFE - Leipzig Research Center for Civilization Diseases, Universität Leipzig, Leipzig, Germany.,Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Erik G Jönsson
- TOP-Norment, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Jurjen J Luykx
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Outpatient Second Opinion Clinic, GGNet Mental Health, Apeldoorn, The Netherlands
| | - Cyrille L Magne
- Psychology Department, Middle Tennessee State University, Murfreesboro, TN, USA.,Literacy Studies Ph.D. Program, Middle Tennessee State University, Mufreesboro, TN, USA
| | - Stephen M Malone
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Jacquelyn L Meyers
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry, Downstate Health Sciences University, Brooklyn, NY, USA.,Department of Psychiatry, State University of New York Downstate Health Sciences University, Brooklyn, NY, USA
| | - Torgeir Moberget
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry, Downstate Health Sciences University, Brooklyn, NY, USA
| | - Christian Sander
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,Chalfont Centre for Epilepsy, Chalfont-St-Peter, UK
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Edwin van Dellen
- Department of Psychiatry, Department of Intensive Care Medicine, Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Marc Via
- Brainlab-Cognitive Neuroscience Research Group, Department of Clinical Psychology and Psychobiology, and Institute of Neurosciences (UBNeuro), Universitat de Barcelona, Barcelona, Spain.,Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Spain
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.,Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
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6
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Macedo A, Gómez C, Rebelo MÂ, Poza J, Gomes I, Martins S, Maturana-Candelas A, Pablo VGD, Durães L, Sousa P, Figueruelo M, Rodríguez M, Pita C, Arenas M, Álvarez L, Hornero R, Lopes AM, Pinto N. Risk Variants in Three Alzheimer's Disease Genes Show Association with EEG Endophenotypes. J Alzheimers Dis 2021; 80:209-223. [PMID: 33522999 PMCID: PMC8075394 DOI: 10.3233/jad-200963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Dementia due to Alzheimer’s disease (AD) is a complex neurodegenerative disorder, which much of heritability remains unexplained. At the clinical level, one of the most common physiological alterations is the slowing of oscillatory brain activity, measurable by electroencephalography (EEG). Relative power (RP) at the conventional frequency bands (i.e., delta, theta, alpha, beta-1, and beta-2) can be considered as AD endophenotypes. Objective: The aim of this work is to analyze the association between sixteen genes previously related with AD: APOE, PICALM, CLU, BCHE, CETP, CR1, SLC6A3, GRIN2
β, SORL1, TOMM40, GSK3
β, UNC5C, OPRD1, NAV2, HOMER2, and IL1RAP, and the slowing of the brain activity, assessed by means of RP at the aforementioned frequency bands. Methods: An Iberian cohort of 45 elderly controls, 45 individuals with mild cognitive impairment, and 109 AD patients in the three stages of the disease was considered. Genomic information and brain activity of each subject were analyzed. Results: The slowing of brain activity was observed in carriers of risk alleles in IL1RAP (rs10212109, rs9823517, rs4687150), UNC5C (rs17024131), and NAV2 (rs1425227, rs862785) genes, regardless of the disease status and situation towards the strongest risk factors: age, sex, and APOE ɛ4 presence. Conclusion: Endophenotypes reduce the complexity of the general phenotype and genetic variants with a major effect on those specific traits may be then identified. The found associations in this work are novel and may contribute to the comprehension of AD pathogenesis, each with a different biological role, and influencing multiple factors involved in brain physiology.
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Affiliation(s)
- Ana Macedo
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,JTA: The Data Scientists, Porto, Portugal
| | - Carlos Gómez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
| | - Miguel Ângelo Rebelo
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Jesús Poza
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Iva Gomes
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Sandra Martins
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | | | | | - Luis Durães
- Associação Portuguesa de Familiares e Amigos de Doentes de Alzheimer, Lavra, Portugal
| | - Patrícia Sousa
- Associação Portuguesa de Familiares e Amigos de Doentes de Alzheimer, Lavra, Portugal
| | - Manuel Figueruelo
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - María Rodríguez
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - Carmen Pita
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - Miguel Arenas
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,CINBIO (Biomedical Research Center), University of Vigo, Vigo, Spain.,Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain
| | - Luis Álvarez
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,Adeneas, Valencia, Spain
| | - Roberto Hornero
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Alexandra M Lopes
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Nádia Pinto
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,Centro de Matemática da Universidade do Porto, Porto, Portugal
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7
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Falsaperla R, Vitaliti G, Marino SD, Praticò AD, Mailo J, Spatuzza M, Cilio MR, Foti R, Ruggieri M. Graph theory in paediatric epilepsy: A systematic review. DIALOGUES IN CLINICAL NEUROSCIENCE 2021; 23:3-13. [PMID: 35860177 PMCID: PMC9286734 DOI: 10.1080/19585969.2022.2043128] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Graph theoretical studies have been designed to investigate network topologies during life. Network science and graph theory methods may contribute to a better understanding of brain function, both normal and abnormal, throughout developmental stages. The degree to which childhood epilepsies exert a significant effect on brain network organisation and cognition remains unclear. The hypothesis suggests that the formation of abnormal networks associated with epileptogenesis early in life causes a disruption in normal brain network development and cognition, reflecting abnormalities in later life. Neurological diseases with onset during critical stages of brain maturation, including childhood epilepsy, may threaten this orderly neurodevelopmental process. According to the hypothesis that the formation of abnormal networks associated with epileptogenesis in early life causes a disruption in normal brain network development, it is then mandatory to perform a proper examination of children with new-onset epilepsy early in the disease course and a deep study of their brain network organisation over time. In regards, graph theoretical analysis could add more information. In order to facilitate further development of graph theory in childhood, we performed a systematic review to describe its application in functional dynamic connectivity using electroencephalographic (EEG) analysis, focussing on paediatric epilepsy.
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Affiliation(s)
- Raffaele Falsaperla
- Neonatal Intensive Care Unit, San Marco Hospital, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
- Unit of Pediatrics and Pediatric Emergency, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Giovanna Vitaliti
- Department of Medical Sciences, Unit of Pediatrics, University of Ferrara, Ferrara, Italy
| | - Simona Domenica Marino
- Unit of Pediatrics and Pediatric Emergency, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Andrea Domenico Praticò
- Unit of Rare Diseases of the Nervous System in Childhood, Department of Clinical and Experimental Medicine, Section of Pediatrics and Child Neuropsychiatry, University of Catania, Catania, Italy
| | - Janette Mailo
- Division of Pediatric Neurology, University of Alberta, Stollery Children’s Hospital, Edmonton, Alberta, Canada
| | - Michela Spatuzza
- National Council of Research, Institute for Biomedical Research and Innovation (IRIB), Unit of Catania, Catania, Italy
| | - Maria Roberta Cilio
- Institute for Experimental and Clinical Research, Catholic University of Leuven, Brussels, Belgium
| | - Rosario Foti
- Department Chief of Rheumatology Unit, San Marco Hospital, University Hospital Policlinico “G. Rodolico-San Marco", Catania, Italy
| | - Martino Ruggieri
- Unit of Rare Diseases of the Nervous System in Childhood, Department of Clinical and Experimental Medicine, Section of Pediatrics and Child Neuropsychiatry, University of Catania, Catania, Italy
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8
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Shu S, Qin L, Yin Y, Han M, Cui W, Gao JH. Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks. Hum Brain Mapp 2020; 41:2160-2172. [PMID: 31961469 PMCID: PMC7267903 DOI: 10.1002/hbm.24937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 01/02/2020] [Accepted: 01/13/2020] [Indexed: 12/21/2022] Open
Abstract
The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.
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Affiliation(s)
- Su Shu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Department of Linguistics, The University of Hong Kong, Hong Kong, China
| | - Yayan Yin
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Meizhen Han
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Wei Cui
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
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9
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Prent N, Smit DJA. The dynamics of resting-state alpha oscillations predict individual differences in creativity. Neuropsychologia 2020; 142:107456. [PMID: 32283066 DOI: 10.1016/j.neuropsychologia.2020.107456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 03/25/2020] [Accepted: 03/29/2020] [Indexed: 12/19/2022]
Abstract
The neuronal mechanisms underlying creativity are poorly understood. Arguably, the brain's ability to switch states would contribute to achieving novel ideas, and thus to creativity. Faster brain-state switching is reflected in the temporal dynamics of functional brain activity. Stronger autocorrelations in brain activity measures can make a brain stay in a certain state for longer periods, whereas low temporal autocorrelations reflect faster state switching. We established the brain's inherent tendency to switch or stay in a resting, no-task condition using 128 channel electroencephalography (EEG). We assessed temporal autocorrelations of the amplitude modulation of the dominant alpha oscillations (8-13 Hz). Creativity was measured by a self-rating, an examiner-rating and the alternative uses task in 40 healthy young adults, which was scored on dimensions of verbal fluency, originality, elaboration, usefulness, and flexibility. For each dimension, the total number of subject-reported alternative uses that matched the criterion was noted (the quantity measure), as well as the proportion of uses that matched the dimensional criterion. A principal components analysis confirmed the two-component structure of quantity and quality. Partial correlation analysis was used controlling for gender and age, and a cluster permutation test was performed to correct for multiple testing. A significant cluster over right central/temporal brain areas was found with a negative correlation between creativity and temporal autocorrelations were observed (p = 0.028). To our knowledge, this is the first demonstration that individual variation in the dynamically changing activity in the brain may offer a neuronal explanation for individual variation in creative ideation.
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Affiliation(s)
- Naomi Prent
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical Center Location AMC, Amsterdam Neuroscience, the Netherlands
| | - Dirk J A Smit
- Psychiatry Department, Amsterdam University Medical Center Location AMC, Amsterdam Neuroscience, the Netherlands.
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10
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Hou Y, Wei Q, Ou R, Yang J, Gong Q, Shang H. Impaired topographic organization in Parkinson's disease with mild cognitive impairment. J Neurol Sci 2020; 414:116861. [PMID: 32387848 DOI: 10.1016/j.jns.2020.116861] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 02/13/2020] [Accepted: 04/24/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is common in Parkinson's disease (PD), and graph theory approaches can be performed to investigate the topographic organization in newly diagnosed drug-naïve PD patients with MCI. METHOD We recruited PD patients with MCI (PD-MCI), PD patients with cognitive unimpaired (PD-CU), and age- and sex-matched healthy controls (HCs). Resting-state functional MRI (fMRI) whole-brain connectivity was examined, and topographic properties were measured with age, sex and education as covariates. Correlation analyses were performed between topographic features and cognitive scores. RESULTS Newly diagnosed drug-naïve PD patients and HCs presented small-world properties, and PD patients had increasing random organizations of brain networks, especially in PD patients with MCI. We also found a descending trend (HC > PD-CU > PD-MCI) in the clustering coefficient (Cp), characteristic path length (Lp) and local efficiency (Eloc), and a rising trend (HC < PD-CU < PD-MCI) in the global efficiency (Eglob). Only PD patients with MCI showed decreased nodal centralities in nodes of the sensorimotor network (SMN), default mode network (DMN), and the ventral anterior prefrontal cortex (vent aPFC), and increased nodal centralities in nodes of the cingulo-opercular network (CON), occipital network, and the ventral lateral prefrontal cortex (vlPFC). The increased nodal centralities in the parietal node of CON negatively correlated with cognitive scores in all PD patients. CONCLUSION Our results suggested that newly diagnosed drug-naïve PD patients had increasing random organizations of brain networks, especially in PD-MCI patients. Nodal changes were mainly observed in PD-MCI patients.
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Affiliation(s)
- Yanbing Hou
- Department of neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qianqian Wei
- Department of neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ruwei Ou
- Department of neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jing Yang
- Department of neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Huifang Shang
- Department of neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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11
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Cartocci G, Maglione AG, Vecchiato G, Modica E, Rossi D, Malerba P, Marsella P, Scorpecci A, Giannantonio S, Mosca F, Leone CA, Grassia R, Babiloni F. Frontal brain asymmetries as effective parameters to assess the quality of audiovisual stimuli perception in adult and young cochlear implant users. ACTA ACUST UNITED AC 2019; 38:346-360. [PMID: 30197426 PMCID: PMC6146571 DOI: 10.14639/0392-100x-1407] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 08/01/2017] [Indexed: 11/23/2022]
Abstract
How is music perceived by cochlear implant (CI) users? This question arises as “the next step” given the impressive performance obtained by these patients in language perception. Furthermore, how can music perception be evaluated beyond self-report rating, in order to obtain measurable data? To address this question, estimation of the frontal electroencephalographic (EEG) alpha activity imbalance, acquired through a 19-channel EEG cap, appears to be a suitable instrument to measure the approach/withdrawal (AW index) reaction to external stimuli. Specifically, a greater value of AW indicates an increased propensity to stimulus approach, and vice versa a lower one a tendency to withdraw from the stimulus. Additionally, due to prelingually and postlingually deafened pathology acquisition, children and adults, respectively, would probably differ in music perception. The aim of the present study was to investigate children and adult CI users, in unilateral (UCI) and bilateral (BCI) implantation conditions, during three experimental situations of music exposure (normal, distorted and mute). Additionally, a study of functional connectivity patterns within cerebral networks was performed to investigate functioning patterns in different experimental populations. As a general result, congruency among patterns between BCI patients and control (CTRL) subjects was seen, characterised by lowest values for the distorted condition (vs. normal and mute conditions) in the AW index and in the connectivity analysis. Additionally, the normal and distorted conditions were significantly different in CI and CTRL adults, and in CTRL children, but not in CI children. These results suggest a higher capacity of discrimination and approach motivation towards normal music in CTRL and BCI subjects, but not for UCI patients. Therefore, for perception of music CTRL and BCI participants appear more similar than UCI subjects, as estimated by measurable and not self-reported parameters.
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Affiliation(s)
- G Cartocci
- Department of Molecular Medicine, Sapienza University of Rome, Italy.,These authors equally contributed to the present article
| | - A G Maglione
- BrainSigns Srl, Rome, Italy.,These authors equally contributed to the present article
| | - G Vecchiato
- Department of Molecular Medicine, Sapienza University of Rome, Italy
| | - E Modica
- Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Italy
| | - D Rossi
- Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Italy
| | - P Malerba
- Cochlear Italia Srl., Bologna, Italy
| | - P Marsella
- Department of Otorhinolaryngology, Audiology and Otology Unit, "Bambino Gesù" Pediatric Hospital, Rome, Italy
| | - A Scorpecci
- Department of Otorhinolaryngology, Audiology and Otology Unit, "Bambino Gesù" Pediatric Hospital, Rome, Italy
| | - S Giannantonio
- Department of Otorhinolaryngology, Audiology and Otology Unit, "Bambino Gesù" Pediatric Hospital, Rome, Italy
| | - F Mosca
- ENT Department, Azienda Ospedaliera Dei Colli Monaldi, Naples, Italy
| | - C A Leone
- ENT Department, Azienda Ospedaliera Dei Colli Monaldi, Naples, Italy
| | - R Grassia
- ENT Department, Azienda Ospedaliera Dei Colli Monaldi, Naples, Italy
| | - F Babiloni
- Department of Molecular Medicine, Sapienza University of Rome, Italy.,BrainSigns Srl, Rome, Italy
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12
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Schetinin V, Jakaite L, Nyah N, Novakovic D, Krzanowski W. Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification. Int J Neural Syst 2018; 28:1750064. [DOI: 10.1142/s0129065717500642] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.
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Affiliation(s)
- Vitaly Schetinin
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Livija Jakaite
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Ndifreke Nyah
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Dusica Novakovic
- School of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, UK
| | - Wojtek Krzanowski
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
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13
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Ashbrook DG, Mulligan MK, Williams RW. Post-genomic behavioral genetics: From revolution to routine. GENES, BRAIN, AND BEHAVIOR 2018; 17:e12441. [PMID: 29193773 PMCID: PMC5876106 DOI: 10.1111/gbb.12441] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 11/02/2017] [Accepted: 11/20/2017] [Indexed: 12/16/2022]
Abstract
What was once expensive and revolutionary-full-genome sequence-is now affordable and routine. Costs will continue to drop, opening up new frontiers in behavioral genetics. This shift in costs from the genome to the phenome is most notable in large clinical studies of behavior and associated diseases in cohorts that exceed hundreds of thousands of subjects. Examples include the Women's Health Initiative (www.whi.org), the Million Veterans Program (www. RESEARCH va.gov/MVP), the 100 000 Genomes Project (genomicsengland.co.uk) and commercial efforts such as those by deCode (www.decode.com) and 23andme (www.23andme.com). The same transition is happening in experimental neuro- and behavioral genetics, and sample sizes of many hundreds of cases are becoming routine (www.genenetwork.org, www.mousephenotyping.org). There are two major consequences of this new affordability of massive omics datasets: (1) it is now far more practical to explore genetic modulation of behavioral differences and the key role of gene-by-environment interactions. Researchers are already doing the hard part-the quantitative analysis of behavior. Adding the omics component can provide powerful links to molecules, cells, circuits and even better treatment. (2) There is an acute need to highlight and train behavioral scientists in how best to exploit new omics approaches. This review addresses this second issue and highlights several new trends and opportunities that will be of interest to experts in animal and human behaviors.
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Affiliation(s)
- D G Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Sciences Center, College of Medicine, Memphis, Tennessee
| | - M K Mulligan
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Sciences Center, College of Medicine, Memphis, Tennessee
| | - R W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Sciences Center, College of Medicine, Memphis, Tennessee
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14
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Vecchio F, Miraglia F, Maria Rossini P. Connectome: Graph theory application in functional brain network architecture. Clin Neurophysiol Pract 2017; 2:206-213. [PMID: 30214997 PMCID: PMC6123924 DOI: 10.1016/j.cnp.2017.09.003] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 07/28/2017] [Accepted: 09/06/2017] [Indexed: 12/20/2022] Open
Abstract
Network science and graph theory applications can help in understanding how human cognitive functions are linked to neuronal network structure. The present review focuses on pivotal recent studies regarding graph theory application on functional dynamic connectivity investigated by electroencephalographic (EEG) analysis. Graph analysis applications represent an interesting probe to analyze the distinctive features of real life by focusing on functional connectivity networks. Application of graph theory to patient data might provide more insight into the pathophysiological processes underlying brain disconnection. Graph theory might aid in monitoring the impact of eventual pharmacological and rehabilitative treatments. Network science and graph theory applications have recently spread widely to help in understanding how human cognitive functions are linked to neuronal network structure, thus providing a conceptual frame that can help in reducing the analytical brain complexity and underlining how network topology can be used to characterize and model vulnerability and resilience to brain disease and dysfunction. The present review focuses on few pivotal recent studies of our research team regarding graph theory application in functional dynamic connectivity investigated by electroencephalographic (EEG) analysis. The article is divided into two parts. The first describes the methodological approach to EEG functional connectivity data analysis. In the second part, network studies of physiological aging and neurological disorders are explored, with a particular focus on epilepsy and neurodegenerative dementias, such as Alzheimer's disease.
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Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Rome, Italy.,Institute of Neurology, Dept. Geriatrics, Neuroscience & Orthopedics, Catholic University, Policlinic A. Gemelli, Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, IRCCS San Raffaele Pisana, Rome, Italy.,Institute of Neurology, Dept. Geriatrics, Neuroscience & Orthopedics, Catholic University, Policlinic A. Gemelli, Rome, Italy
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15
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Klein M, Onnink M, van Donkelaar M, Wolfers T, Harich B, Shi Y, Dammers J, Arias-Vásquez A, Hoogman M, Franke B. Brain imaging genetics in ADHD and beyond - Mapping pathways from gene to disorder at different levels of complexity. Neurosci Biobehav Rev 2017; 80:115-155. [PMID: 28159610 PMCID: PMC6947924 DOI: 10.1016/j.neubiorev.2017.01.013] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 12/08/2016] [Accepted: 01/09/2017] [Indexed: 01/03/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a common and often persistent neurodevelopmental disorder. Beyond gene-finding, neurobiological parameters, such as brain structure, connectivity, and function, have been used to link genetic variation to ADHD symptomatology. We performed a systematic review of brain imaging genetics studies involving 62 ADHD candidate genes in childhood and adult ADHD cohorts. Fifty-one eligible research articles described studies of 13 ADHD candidate genes. Almost exclusively, single genetic variants were studied, mostly focussing on dopamine-related genes. While promising results have been reported, imaging genetics studies are thus far hampered by methodological differences in study design and analysis methodology, as well as limited sample sizes. Beyond reviewing imaging genetics studies, we also discuss the need for complementary approaches at multiple levels of biological complexity and emphasize the importance of combining and integrating findings across levels for a better understanding of biological pathways from gene to disease. These may include multi-modal imaging genetics studies, bioinformatic analyses, and functional analyses of cell and animal models.
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Affiliation(s)
- Marieke Klein
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Marten Onnink
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Marjolein van Donkelaar
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Thomas Wolfers
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Benjamin Harich
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Yan Shi
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Janneke Dammers
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Department of Psychiatry, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Alejandro Arias-Vásquez
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Department of Psychiatry, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Martine Hoogman
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands; Department of Psychiatry, Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
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16
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Demuru M, Gouw AA, Hillebrand A, Stam CJ, van Dijk BW, Scheltens P, Tijms BM, Konijnenberg E, Ten Kate M, den Braber A, Smit DJA, Boomsma DI, Visser PJ. Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs. Sci Rep 2017; 7:9685. [PMID: 28852152 PMCID: PMC5575140 DOI: 10.1038/s41598-017-10235-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 08/01/2017] [Indexed: 01/08/2023] Open
Abstract
Resting-state functional connectivity patterns are highly stable over time within subjects. This suggests that such 'functional fingerprints' may have strong genetic component. We investigated whether the functional (FC) or effective (EC) connectivity patterns of one monozygotic twin could be used to identify the co-twin among a larger sample and determined the overlap in functional fingerprints within monozygotic (MZ) twin pairs using resting state magnetoencephalography (MEG). We included 32 cognitively normal MZ twin pairs from the Netherlands Twin Register who participate in the EMIF-AD preclinAD study (average age 68 years). Combining EC information across multiple frequency bands we obtained an identification rate over 75%. Since MZ twin pairs are genetically identical these results suggest a high genetic contribution to MEG-based EC patterns, leading to large similarities in brain connectivity patterns between two individuals even after 60 years of life or more.
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Affiliation(s)
- M Demuru
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
| | - A A Gouw
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - C J Stam
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - B W van Dijk
- Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center, Amsterdam, The Netherlands
| | - P Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - B M Tijms
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - E Konijnenberg
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - M Ten Kate
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - A den Braber
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - D J A Smit
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Academic Medical Center, Amsterdam, The Netherlands
| | - D I Boomsma
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - P J Visser
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
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17
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Holla B, Panda R, Venkatasubramanian G, Biswal B, Bharath RD, Benegal V. Disrupted resting brain graph measures in individuals at high risk for alcoholism. Psychiatry Res Neuroimaging 2017; 265:54-64. [PMID: 28531764 DOI: 10.1016/j.pscychresns.2017.05.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 05/03/2017] [Accepted: 05/04/2017] [Indexed: 01/13/2023]
Abstract
Familial susceptibility to alcoholism is likely to be linked to the externalizing diathesis seen in high-risk offspring from high-density alcohol use disorder (AUD) families. The present study aimed at comparing resting brain functional connectivity and their association with externalizing symptoms and alcoholism familial density in 40 substance-naive high-risk (HR) male offspring from high-density AUD families and 30 matched healthy low-risk (LR) males without a family history of substance dependence using graph theory-based network analysis. The HR subjects from high-density AUD families compared with LR, showed significantly reduced clustering, small-worldness, and local network efficiency. The frontoparietal, cingulo-opercular, sensorimotor and cerebellar networks exhibited significantly reduced functional segregation. These disruptions exhibited independent incremental value in predicting the externalizing symptoms over and above the demographic variables. The reduction of functional segregation in HR subjects was significant across both the younger and older age groups and was proportional to the family loading of AUDs. Detection and estimation of these developmentally relevant disruptions in small-world architecture at critical brain regions sub-serving cognitive, affective, and sensorimotor processes are vital for understanding the familial risk for early onset alcoholism as well as for understanding the pathophysiological mechanism of externalizing behaviors.
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Affiliation(s)
- Bharath Holla
- Centre for Addiction Medicine, Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore, India.
| | - Rajanikant Panda
- Cognitive Neuroscience Centre and Department of Neuroimaging and Interventional Radiology (NIIR), NIMHANS, Hosur Road, Bangalore, India
| | | | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology (NJIT), University Heights, Newark, NJ, USA
| | - Rose Dawn Bharath
- Cognitive Neuroscience Centre and Department of Neuroimaging and Interventional Radiology (NIIR), NIMHANS, Hosur Road, Bangalore, India.
| | - Vivek Benegal
- Centre for Addiction Medicine, Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore, India.
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18
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Mirzakhalili E, Gourgou E, Booth V, Epureanu B. Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies. Front Neural Circuits 2017; 11:38. [PMID: 28659765 PMCID: PMC5468411 DOI: 10.3389/fncir.2017.00038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons.
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Affiliation(s)
- Ehsan Mirzakhalili
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
| | - Eleni Gourgou
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States.,Division of Geriatrics, Department of Internal Medicine, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Victoria Booth
- Department of Mathematics, University of MichiganAnn Arbor, MI, United States.,Department of Anesthesiology, Medical School, University of MichiganAnn Arbor, MI, United States
| | - Bogdan Epureanu
- Department of Mechanical Engineering, University of MichiganAnn Arbor, MI, United States
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19
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Lopes R, Delmaire C, Defebvre L, Moonen AJ, Duits AA, Hofman P, Leentjens AF, Dujardin K. Cognitive phenotypes in parkinson's disease differ in terms of brain-network organization and connectivity. Hum Brain Mapp 2017; 38:1604-1621. [PMID: 27859960 PMCID: PMC6867173 DOI: 10.1002/hbm.23474] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 10/28/2016] [Accepted: 11/08/2016] [Indexed: 11/07/2022] Open
Abstract
Cognitive deficits are common in Parkinson's disease and we suspect that dysfunctions of connected brain regions can be the source of these deficits. The aim of the present study was to investigate changes in whole-brain intrinsic functional connectivity according to differences in cognitive profiles in Parkinson's disease. 119 participants were enrolled and divided into four groups according to their cognitive phenotypes (determined by a cluster analysis): (i) 31 cognitively intact patients (G1), (ii) 31 patients with only slight mental slowing (G2), (iii) 43 patients with mild to moderate deficits mainly in executive functions (G3), (iv) 14 patients with severe deficits in all cognitive domains (G4-5). Rs-fMRI whole-brain connectivity was examined by two complementary approaches: graph theory for studying network functional organization and network-based statistics (NBS) for exploring functional connectivity amongst brain regions. After adjustment for age, duration of formal education and center of acquisition, there were significant group differences for all functional organization indexes: functional organization decreased (G1 > G2 > G3 > G4-5) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, P < 0.01) mainly concerned the ventral prefrontal, parietal, temporal and occipital cortices as well as the basal ganglia. In Parkinson's disease, brain network organization is progressively disrupted as cognitive impairment worsens, with an increasing number of altered connections between brain regions. We observed reduced connectivity in highly associative areas, even in patients with only slight mental slowing. The association of slowed mental processing with loss of connectivity between highly associative areas could be an early marker of cognitive impairment in Parkinson's disease and may contribute to the detection of prodromal forms of Parkinson's disease dementia. Hum Brain Mapp 38:1604-1621, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Renaud Lopes
- Univ. Lille, U1171 ‐ Degenerative & vascular cognitive disordersLilleFrance
- Inserm, U1171LilleFrance
- Neuroimaging DepartmentCHU LilleLilleFrance
| | - Christine Delmaire
- Univ. Lille, U1171 ‐ Degenerative & vascular cognitive disordersLilleFrance
- Inserm, U1171LilleFrance
- Neuroimaging DepartmentCHU LilleLilleFrance
| | - Luc Defebvre
- Univ. Lille, U1171 ‐ Degenerative & vascular cognitive disordersLilleFrance
- Inserm, U1171LilleFrance
- Neurology and Movement Disorders DepartmentCHU LilleLilleFrance
| | - Anja J. Moonen
- Department of PsychiatryMaastricht University Medical CentreMaastrichtthe Netherlands
| | - Annelien A. Duits
- Department of PsychiatryMaastricht University Medical CentreMaastrichtthe Netherlands
| | - Paul Hofman
- Department of RadiologyMaastricht University Medical CentreMaastrichtthe Netherlands
| | - Albert F.G. Leentjens
- Department of PsychiatryMaastricht University Medical CentreMaastrichtthe Netherlands
| | - Kathy Dujardin
- Univ. Lille, U1171 ‐ Degenerative & vascular cognitive disordersLilleFrance
- Inserm, U1171LilleFrance
- Neurology and Movement Disorders DepartmentCHU LilleLilleFrance
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20
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Miraglia F, Vecchio F, Rossini PM. Searching for signs of aging and dementia in EEG through network analysis. Behav Brain Res 2017; 317:292-300. [DOI: 10.1016/j.bbr.2016.09.057] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 09/23/2016] [Accepted: 09/24/2016] [Indexed: 12/20/2022]
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Gkigkitzis I, Haranas I, Kotsireas I. Biological Relevance of Network Architecture. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 988:1-29. [PMID: 28971385 DOI: 10.1007/978-3-319-56246-9_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Mathematical representations of brain networks in neuroscience through the use of graph theory may be very useful for the understanding of neurological diseases and disorders and such an explanatory power is currently under intense investigation. Graph metrics are expected to vary across subjects and are likely to reflect behavioural and cognitive performances. The challenge is to set up a framework that can explain how behaviour, cognition, memory, and other brain properties can emerge through the combined interactions of neurons, ensembles of neurons, and larger-scale brain regions that make information transfer possible. "Hidden" graph theoretic properties in the construction of brain networks may limit or enhance brain functionality and may be representative of aspects of human psychology. As theorems emerge from simple mathematical properties of graphs, similarly, cognition and behaviour may emerge from the molecular, cellular and brain region substrate interactions. In this review report, we identify some studies in the current literature that have used graph theoretical metrics to extract neurobiological conclusions, we briefly discuss the link with the human connectome project as an effort to integrate human data that may aid the study of emergent patterns and we suggest a way to start categorizing diseases according to their brain network pathologies as these are measured by graph theory.
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Affiliation(s)
- Ioannis Gkigkitzis
- Department of Mathematics, East Carolina University, 124 Austin Building, East Fifth Street, Greenville, NC, 27858-4353, USA.
| | - Ioannis Haranas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
| | - Ilias Kotsireas
- Department of Physics and Computer Science, Wilfrid Laurier University, Science Building, Room N2078, 75 University Ave. W., Waterloo, ON, Canada, N2L 3C5
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22
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Iacono WG, Malone SM, Vrieze SI. Endophenotype best practices. Int J Psychophysiol 2017; 111:115-144. [PMID: 27473600 PMCID: PMC5219856 DOI: 10.1016/j.ijpsycho.2016.07.516] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 07/21/2016] [Accepted: 07/24/2016] [Indexed: 01/19/2023]
Abstract
This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.
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Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins. Int J Psychophysiol 2016; 115:133-141. [PMID: 28043892 DOI: 10.1016/j.ijpsycho.2016.12.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 11/25/2016] [Accepted: 12/28/2016] [Indexed: 02/04/2023]
Abstract
Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization.
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24
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Smit DJ, de Geus EJ, Boersma M, Boomsma DI, Stam CJ. Life-Span Development of Brain Network Integration Assessed with Phase Lag Index Connectivity and Minimum Spanning Tree Graphs. Brain Connect 2016; 6:312-25. [DOI: 10.1089/brain.2015.0359] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Dirk J.A. Smit
- Biological Psychology, VU University, Amsterdam, The Netherlands
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
| | - Eco J.C. de Geus
- Biological Psychology, VU University, Amsterdam, The Netherlands
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
- EMGO+ Institute, VU Medical Centre, Amsterdam, The Netherlands
| | - Maria Boersma
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dorret I. Boomsma
- Biological Psychology, VU University, Amsterdam, The Netherlands
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
- EMGO+ Institute, VU Medical Centre, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
- Clinical Neurophysiology, VU University Medical Centre, Amsterdam, The Netherlands
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25
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Antonakakis M, Zervakis M, van Beijsterveldt CE, Boomsma DI, De Geus EJ, Micheloyannis S, Smit DJ. Genetic effects on source level evoked and induced oscillatory brain responses in a visual oddball task. Biol Psychol 2016; 114:69-80. [DOI: 10.1016/j.biopsycho.2015.12.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 11/28/2015] [Accepted: 12/22/2015] [Indexed: 12/31/2022]
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26
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Vollebregt MA, van Dongen-Boomsma M, Slaats-Willemse D, Buitelaar JK, Oostenveld R. How the Individual Alpha Peak Frequency Helps Unravel the Neurophysiologic Underpinnings of Behavioral Functioning in Children With Attention-Deficit/Hyperactivity Disorder. Clin EEG Neurosci 2015; 46:285-91. [PMID: 25392007 DOI: 10.1177/1550059414537257] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Accepted: 04/30/2014] [Indexed: 11/16/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) has been associated with an elevated resting-state theta/beta power ratio and elevated theta power. However, the potential confounding effect of a low individual alpha peak frequency (IAPF) on the theta-power estimate has often been disregarded when studying the relationship between ADHD and the theta/beta power ratio or theta power alone. The aim of the present study was to assess whether the theta/beta power ratio and relative theta power are correlated with behavioral functioning in children with ADHD, as expected from previous studies. Subsequently, the influence of IAPF and the amount of supposed overlap between the individually determined alpha-band and the fixed theta-band were studied. For 38 children (aged 8-15 years), electroencephalographic (EEG) and investigator-scored ADHD Rating Scale IV data were available. Additional neurocognitive data were available for 32 children. As expected, the theta/beta power ratio and theta were positively related to the ADHD core symptoms. This relationship strengthened when controlling for IAPF, although correlations did not significantly differ from one another. Eight of 38 children (21%) showed a supposed overlap between their individually determined alpha band and the theta band. Neurocognitive performance did not show any relationship with the theta/beta power ratio or theta. The results of this study confirm that the theta/beta power ratio and theta power are indeed correlated with behavioral symptoms in children with ADHD and underscore the relevance of taking the IAPF into account.
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Affiliation(s)
- Madelon A Vollebregt
- Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, Netherlands Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, Netherlands Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, Netherlands
| | - Martine van Dongen-Boomsma
- Radboud University Medical Centre, Department of Psychiatry, Nijmegen, Netherlands Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, Netherlands Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, Netherlands
| | - Dorine Slaats-Willemse
- Radboud University Medical Centre, Department of Psychiatry, Nijmegen, Netherlands Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, Netherlands Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, Netherlands
| | - Jan K Buitelaar
- Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, Netherlands Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, Netherlands Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, Netherlands
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, Netherlands
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27
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Age-related differences in electroencephalogram connectivity and network topology. Neurobiol Aging 2015; 36:1849-59. [DOI: 10.1016/j.neurobiolaging.2015.02.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2014] [Revised: 01/11/2015] [Accepted: 02/05/2015] [Indexed: 01/08/2023]
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28
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Yang CY, Lin CP. Time-Varying Network Measures in Resting and Task States Using Graph Theoretical Analysis. Brain Topogr 2015; 28:529-40. [PMID: 25877489 DOI: 10.1007/s10548-015-0432-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2014] [Accepted: 04/07/2015] [Indexed: 11/24/2022]
Abstract
Recent studies have shown the importance of graph theory in analyzing characteristic features of functional networks of the human brain. However, many of these explorations have focused on static patterns of a representative graph that describe the relatively long-term brain activity. Therefore, this study established and characterized functional networks based on the synchronization likelihood and graph theory. Quasidynamic graphs were constructed simply by dividing a long-term static graph into a sequence of subgraphs that each had a timescale of 1 s. Irregular changes were then used to investigate differences in human brain networks between resting and math-operation states using magnetoencephalography, which may provide insights into the functional substrates underlying logical reasoning. We found that graph properties could differ from brain frequency rhythms, with a higher frequency indicating a lower small-worldness, while changes in human brain state altered the functional networks into more-centralized and segregated distributions according to the task requirements. Time-varying connectivity maps could provide detailed information about the structure distribution. The frontal theta activity represents the essential foundation and may subsequently interact with high-frequency activity in cognitive processing.
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Affiliation(s)
- Chia-Yen Yang
- Department of Biomedical Engineering, Ming-Chuan University, Taoyuan, Taiwan,
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29
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Berchicci M, Tamburro G, Comani S. The intrahemispheric functional properties of the developing sensorimotor cortex are influenced by maturation. Front Hum Neurosci 2015; 9:39. [PMID: 25741263 PMCID: PMC4330894 DOI: 10.3389/fnhum.2015.00039] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 01/14/2015] [Indexed: 12/28/2022] Open
Abstract
The investigation of the functional changes in the sensorimotor cortex has important clinical implications as deviations from normal development can anticipate developmental disorders. The functional properties of the sensorimotor cortex can be characterized through the rolandic mu rhythm, already present during infancy. However, how the sensorimotor network develops from early infancy to adulthood, and how sensorimotor processing contributes to the generation of perceptual-motor coupling remains largely unknown. Here, we analyzed magnetoencephalographic (MEG) data recorded in two groups of infants (11-24 and 26-47 weeks), two groups of children (24-34 and 36-60 months), and a control group of adults (20-39 years), during intermixed conditions of rest and prehension. The MEG sensor array was positioned over the sensorimotor cortex of the contralateral hemisphere. We characterized functional connectivity and topological properties of the sensorimotor network across ages and conditions through synchronization likelihood and segregation/integration measures in an individual mu rhythm frequency range. All functional measures remained almost unchanged during the first year of life, whereas they varied afterwards through childhood to reach adult values, demonstrating an increase of both segregation and integration properties. With age, the sensorimotor network evolved from a more random (infants) to a "small-world" organization (children and adults), more efficient both locally and globally. These findings are in line with prior studies on structural and functional brain development in infants, children and adults. We could not demonstrate any significant change in the functional properties of the sensorimotor cortex in the prehension condition with respect to rest. Our results support the view that, since early infancy, the functional properties of the developing sensorimotor cortex are modulated by maturation.
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Affiliation(s)
- Marika Berchicci
- BIND - Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara Chieti, Italy ; Department of Movement, Human and Health Sciences, University of Rome "Foro Italico," Rome, Italy
| | - Gabriella Tamburro
- BIND - Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara Chieti, Italy ; Department of Medicine and Aging Sciences, University "G. d'Annunzio" of Chieti-Pescara Chieti, Italy
| | - Silvia Comani
- BIND - Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara Chieti, Italy ; Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara Chieti, Italy ; Casa di Cura Privata Villa Serena Città Sant'Angelo, Italy
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30
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Birca A, Lassonde M, Lippé S, Lortie A, Vannasing P, Carmant L. Enhanced EEG connectivity in children with febrile seizures. Epilepsy Res 2015; 110:32-8. [DOI: 10.1016/j.eplepsyres.2014.11.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 10/23/2014] [Accepted: 11/11/2014] [Indexed: 01/09/2023]
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31
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Schmidt H, Petkov G, Richardson MP, Terry JR. Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity. PLoS Comput Biol 2014; 10:e1003947. [PMID: 25393751 PMCID: PMC4230731 DOI: 10.1371/journal.pcbi.1003947] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 09/26/2014] [Indexed: 12/16/2022] Open
Abstract
Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.
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Affiliation(s)
- Helmut Schmidt
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - George Petkov
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | | | - John R. Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- * E-mail:
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32
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Chowdhury FA, Woldman W, FitzGerald THB, Elwes RDC, Nashef L, Terry JR, Richardson MP. Revealing a brain network endophenotype in families with idiopathic generalised epilepsy. PLoS One 2014; 9:e110136. [PMID: 25302690 PMCID: PMC4193864 DOI: 10.1371/journal.pone.0110136] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 09/17/2014] [Indexed: 12/16/2022] Open
Abstract
Idiopathic generalised epilepsy (IGE) has a genetic basis. The mechanism of seizure expression is not fully known, but is assumed to involve large-scale brain networks. We hypothesised that abnormal brain network properties would be detected using EEG in patients with IGE, and would be manifest as a familial endophenotype in their unaffected first-degree relatives. We studied 117 participants: 35 patients with IGE, 42 unaffected first-degree relatives, and 40 normal controls, using scalp EEG. Graph theory was used to describe brain network topology in five frequency bands for each subject. Frequency bands were chosen based on a published Spectral Factor Analysis study which demonstrated these bands to be optimally robust and independent. Groups were compared, using Bonferroni correction to account for nonindependent measures and multiple groups. Degree distribution variance was greater in patients and relatives than controls in the 6-9 Hz band (p = 0.0005, p = 0.0009 respectively). Mean degree was greater in patients than healthy controls in the 6-9 Hz band (p = 0.0064). Clustering coefficient was higher in patients and relatives than controls in the 6-9 Hz band (p = 0.0025, p = 0.0013). Characteristic path length did not differ between groups. No differences were found between patients and unaffected relatives. These findings suggest brain network topology differs between patients with IGE and normal controls, and that some of these network measures show similar deviations in patients and in unaffected relatives who do not have epilepsy. This suggests brain network topology may be an inherited endophenotype of IGE, present in unaffected relatives who do not have epilepsy, as well as in affected patients. We propose that abnormal brain network topology may be an endophenotype of IGE, though not in itself sufficient to cause epilepsy.
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Affiliation(s)
- Fahmida A. Chowdhury
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Centre for Epilepsy, King's College Hospital, London, United Kingdom
| | - Wessel Woldman
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Thomas H. B. FitzGerald
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Wellcome Trust Centre for Neuroimaging, UCL, London, United Kingdom
| | | | - Lina Nashef
- Centre for Epilepsy, King's College Hospital, London, United Kingdom
| | - John R. Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Mark P. Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Centre for Epilepsy, King's College Hospital, London, United Kingdom
- * E-mail:
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33
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Abstract
Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.
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Affiliation(s)
- Cornelis J Stam
- Department of Neurology and Clinical Neurophysiology, MEG Center, VU University Medical Center, De Boelelaan 1118, 1081HV Amsterdam, The Netherlands
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34
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Genetic psychophysiology: advances, problems, and future directions. Int J Psychophysiol 2014; 93:173-97. [PMID: 24739435 DOI: 10.1016/j.ijpsycho.2014.04.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Revised: 02/10/2014] [Accepted: 04/07/2014] [Indexed: 12/20/2022]
Abstract
This paper presents an overview of historical advances and the current state of genetic psychophysiology, a rapidly developing interdisciplinary research linking genetics, brain, and human behavior, discusses methodological problems, and outlines future directions of research. The main goals of genetic psychophysiology are to elucidate the neural pathways and mechanisms mediating genetic influences on cognition and emotion, identify intermediate brain-based phenotypes for psychopathology, and provide a functional characterization of genes being discovered by large association studies of behavioral phenotypes. Since the initiation of this neurogenetic approach to human individual differences in the 1970s, numerous twin and family studies have provided strong evidence for heritability of diverse aspects of brain function including resting-state brain oscillations, functional connectivity, and event-related neural activity in a variety of cognitive and emotion processing tasks, as well as peripheral psychophysiological responses. These data indicate large differences in the presence and strength of genetic influences across measures and domains, permitting the selection of heritable characteristics for gene finding studies. More recently, candidate gene association studies began to implicate specific genetic variants in different aspects of neurocognition. However, great caution is needed in pursuing this line of research due to its demonstrated proneness to generate false-positive findings. Recent developments in methods for physiological signal analysis, hemodynamic imaging, and genomic technologies offer new exciting opportunities for the investigation of the interplay between genetic and environmental factors in the development of individual differences in behavior, both normal and abnormal.
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35
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Arns M, Olbrich S. Personalized Medicine in ADHD and Depression: Use of Pharmaco-EEG. Curr Top Behav Neurosci 2014; 21:345-370. [PMID: 24615541 DOI: 10.1007/7854_2014_295] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This chapter summarises recent developments on personalised medicine in psychiatry with a focus on ADHD and depression and their associated biomarkers and phenotypes. Several neurophysiological subtypes in ADHD and depression and their relation to treatment outcome are reviewed. The first important subgroup consists of the 'impaired vigilance' subgroup with often-reported excess frontal theta or alpha activity. This EEG subtype explains ADHD symptoms well based on the EEG Vigilance model, and these ADHD patients responds well to stimulant medication. In depression this subtype might be unresponsive to antidepressant treatments, and some studies suggest these depressive patients might respond better to stimulant medication. Further research should investigate whether sleep problems underlie this impaired vigilance subgroup, thereby perhaps providing a route to more specific treatments for this subgroup. Finally, a slow individual alpha peak frequency is an endophenotype associated with treatment resistance in ADHD and depression. Future studies should incorporate this endophenotype in clinical trials to investigate further the efficacy of new treatments in this substantial subgroup of patients.
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Affiliation(s)
- Martijn Arns
- Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands,
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36
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Long-range temporal correlations in resting-state α oscillations predict human timing-error dynamics. J Neurosci 2013; 33:11212-20. [PMID: 23825424 DOI: 10.1523/jneurosci.2816-12.2013] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Human behavior is imperfect. This is notably clear during repetitive tasks in which sequences of errors or deviations from perfect performance result. These errors are not random, but show patterned fluctuations with long-range temporal correlations that are well described using power-law spectra P(f)∝1/f(β), where β is the power-law scaling exponent describing the decay in temporal correlations. The neural basis of temporal correlations in such behaviors is not known. Interestingly, long-range temporal correlations are a hallmark of amplitude fluctuations in resting-state neuronal oscillations. Here, we investigated whether the temporal dynamics in brain and behavior are related. Thirty-nine subjects' eyes-open rest EEG was measured. Next, subjects reproduced without feedback a 1 s interval by tapping with their right index finger. In line with previous reports, we found evidence for the presence of long-range temporal correlations both in the amplitude modulation of resting-state oscillations in multiple frequency bands and in the timing-error sequences. Frequency scaling exponents of finger tapping and amplitude modulation of oscillations exhibited large individual differences. Neuronal dynamics of resting-state alpha-band oscillations (9-13 Hz) recorded at precentral sites strongly predicted scaling exponents of tapping behavior. The results suggest that individual variation in resting-state brain dynamics offer a neural explanation for individual variation in the error dynamics of human behavior.
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37
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Siebenhühner F, Weiss SA, Coppola R, Weinberger DR, Bassett DS. Intra- and inter-frequency brain network structure in health and schizophrenia. PLoS One 2013; 8:e72351. [PMID: 23991097 PMCID: PMC3753323 DOI: 10.1371/journal.pone.0072351] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Accepted: 07/08/2013] [Indexed: 01/22/2023] Open
Abstract
Empirical studies over the past two decades have provided support for the hypothesis that schizophrenia is characterized by altered connectivity patterns in functional brain networks. These alterations have been proposed as genetically mediated diagnostic biomarkers and are thought to underlie altered cognitive functions such as working memory. However, the nature of this dysconnectivity remains far from understood. In this study, we perform an extensive analysis of functional connectivity patterns extracted from MEG data in 14 subjects with schizophrenia and 14 healthy controls during a 2-back working memory task. We investigate uni-, bi- and multivariate properties of sensor time series by computing wavelet entropy of and correlation between time series, and by constructing binary networks of functional connectivity both within and between classical frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]). Networks are based on the mutual information between wavelet time series, and estimated for each trial window separately, enabling us to consider both network topology and network dynamics. We observed significant decreases in time series entropy and significant increases in functional connectivity in the schizophrenia group in comparison to the healthy controls and identified an inverse relationship between these measures across both subjects and sensors that varied over frequency bands and was more pronounced in controls than in patients. The topological organization of connectivity was altered in schizophrenia specifically in high frequency [Formula: see text] and [Formula: see text] band networks as well as in the [Formula: see text]-[Formula: see text] cross-frequency networks. Network topology varied over trials to a greater extent in patients than in controls, suggesting disease-associated alterations in dynamic network properties of brain function. Our results identify signatures of aberrant neurophysiological behavior in schizophrenia across uni-, bi- and multivariate scales and lay the groundwork for further clinical studies that might lead to the discovery of new intermediate phenotypes.
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Affiliation(s)
- Felix Siebenhühner
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
- Neuroscience Center, University of Helsinki, Helsinki, Finland
| | - Shennan A. Weiss
- Department of Neurology, Columbia University, New York, New York, United States of America
| | - Richard Coppola
- MEG Core Facility, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Daniel R. Weinberger
- Genes, Cognition and Psychosis Program, Clinical Brain Disorders Branch, National Institute of Mental Health, Bethesda, Maryland, United States of America
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America
| | - Danielle S. Bassett
- Department of Physics, University of California Santa Barbara, Santa Barbara, California, United States of America
- Sage Center for the Study of the Mind, University of California Santa Barbara, Santa Barbara, California, United States of America
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Boersma M, Kemner C, de Reus MA, Collin G, Snijders TM, Hofman D, Buitelaar JK, Stam CJ, van den Heuvel MP. Disrupted functional brain networks in autistic toddlers. Brain Connect 2013; 3:41-9. [PMID: 23259692 DOI: 10.1089/brain.2012.0127] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions and aberrant basic information processing. Aberrant brain connectivity patterns have indeed been hypothesized to be a key neural underpinning of autism. In this study, graph analytical tools are used to explore the possible deviant functional brain network organization in autism at a very early stage of brain development. Electroencephalography (EEG) recordings in 12 toddlers with autism (mean age 3.5 years) and 19 control subjects were used to assess interregional functional brain connectivity, with functional brain networks constructed at the level of temporal synchronization between brain regions underlying the EEG electrodes. Children with autism showed a significantly increased normalized path length and reduced normalized clustering, suggesting a reduced global communication capacity already during early brain development. In addition, whole brain connectivity was found to be significantly reduced in these young patients suggesting an overall under-connectivity of functional brain networks in autism. Our findings support the hypothesis of abnormal neural communication in autism, with deviating effects already present at the early stages of brain development.
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Affiliation(s)
- Maria Boersma
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands.
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Abstract
We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis - network clustering and average path length - are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27-74%) and cluster coefficient and path length in the alpha and theta band (40-44% and 23-40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.
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Lexical decision as an endophenotype for reading comprehension: an exploration of an association. Dev Psychopathol 2013; 24:1345-60. [PMID: 23062302 DOI: 10.1017/s0954579412000752] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Based on numerous suggestions in the literature, we evaluated lexical decision (LD) as a putative endophenotype for reading comprehension by investigating heritability estimates and segregation analyses parameter estimates for both of these phenotypes. Specifically, in a segregation analysis of a large sample of families, we established that there is little to no overlap between genes contributing to LD and reading comprehension and that the genetic mechanism behind LD derived from this analysis appears to be more complex than that for reading comprehension. We conclude that in our sample, LD is not a good candidate as an endophenotype for reading comprehension, despite previous suggestions from the literature. Based on this conclusion, we discuss the role and benefit of the endophenotype approach in studies of complex human cognitive functions.
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Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity. Proc Natl Acad Sci U S A 2013; 110:4768-73. [PMID: 23471985 DOI: 10.1073/pnas.1216206110] [Citation(s) in RCA: 112] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.
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Barclay NL, Gregory AM. Quantitative genetic research on sleep: A review of normal sleep, sleep disturbances and associated emotional, behavioural, and health-related difficulties. Sleep Med Rev 2013; 17:29-40. [DOI: 10.1016/j.smrv.2012.01.008] [Citation(s) in RCA: 101] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Revised: 01/19/2012] [Accepted: 01/30/2012] [Indexed: 11/30/2022]
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Boersma M, Smit DJ, Boomsma DI, De Geus EJ, Delemarre-van de Waal HA, Stam CJ. Growing Trees in Child Brains: Graph Theoretical Analysis of Electroencephalography-Derived Minimum Spanning Tree in 5- and 7-Year-Old Children Reflects Brain Maturation. Brain Connect 2013; 3:50-60. [DOI: 10.1089/brain.2012.0106] [Citation(s) in RCA: 129] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Maria Boersma
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
| | - Dirk J.A. Smit
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
- Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Dorret I. Boomsma
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
- Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Eco J.C. De Geus
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
- Biological Psychology, VU University, Amsterdam, The Netherlands
| | | | - Cornelis J. Stam
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands
- Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands
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Structure out of chaos: functional brain network analysis with EEG, MEG, and functional MRI. Eur Neuropsychopharmacol 2013; 23:7-18. [PMID: 23158686 DOI: 10.1016/j.euroneuro.2012.10.010] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2011] [Revised: 09/10/2012] [Accepted: 10/18/2012] [Indexed: 01/21/2023]
Abstract
The brain is the characteristic of a complex structure. By representing brain function, measured with EEG, MEG, and fMRI, as an abstract network, methods for the study of complex systems can be applied. These network studies have revealed insights in the complex, yet organized, architecture that is evidently present in brain function. We will discuss some technical aspects of formation and assessment of the functional brain networks. Moreover, the results that have been reported in this respect in the last years, in healthy brains as well as in functional brain networks of subjects with a neurological or psychiatrical disease, will be reviewed.
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Ethridge LE, Malone SM, Iacono WG, Clementz BA. Genetic influences on composite neural activations supporting visual target identification. Biol Psychol 2012. [PMID: 23201034 DOI: 10.1016/j.biopsycho.2012.11.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Behavior genetic studies of brain activity associated with complex cognitive operations may further elucidate the genetic and physiological underpinnings of basic and complex neural processing. In the present project, monozygotic (N=51 pairs) and dizygotic (N=48 pairs) twins performed a visual oddball task with dense-array EEG. Using spatial PCA, two principal components each were retained for targets and standards; wavelets were used to obtain time-frequency maps of eigenvalue-weighted event-related oscillations for each individual. Distribution of inter-trial phase coherence (ITC) and single trial power (STP) over time indicated that the early principal component was primarily associated with ITC while the later component was associated with a mixture of ITC and STP. Spatial PCA on point-by-point broad sense heritability matrices revealed data-derived frequency bands similar to those well established in EEG literature. Biometric models of eigenvalue-weighted time-frequency data suggest a link between physiology of oscillatory brain activity and patterns of genetic influence.
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Affiliation(s)
- Lauren E Ethridge
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA 30602, United States
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van Dellen E, Douw L, Hillebrand A, Ris-Hilgersom IHM, Schoonheim MM, Baayen JC, De Witt Hamer PC, Velis DN, Klein M, Heimans JJ, Stam CJ, Reijneveld JC. MEG network differences between low- and high-grade glioma related to epilepsy and cognition. PLoS One 2012; 7:e50122. [PMID: 23166829 PMCID: PMC3498183 DOI: 10.1371/journal.pone.0050122] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2012] [Accepted: 10/19/2012] [Indexed: 11/19/2022] Open
Abstract
Objective To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups. Methods We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients. Results LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4–8 Hz), similar to NGL patients. HGG patients’ networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients. Conclusion Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients’ networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline.
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Affiliation(s)
- Edwin van Dellen
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
- * E-mail:
| | - Linda Douw
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Irene H. M. Ris-Hilgersom
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Johannes C. Baayen
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Demetrios N. Velis
- Department of Clinical Neurophysiology and Epilepsy Monitoring Unit, Dutch Epilepsy Clinics Foundation, Heemstede, The Netherlands
| | - Martin Klein
- Department of Medical Psychology, VU University Medical Center, Amsterdam, The Netherlands
| | - Jan J. Heimans
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Jaap C. Reijneveld
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
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Individual differences in EEG spectral power reflect genetic variance in gray and white matter volumes. Twin Res Hum Genet 2012; 15:384-92. [PMID: 22856372 DOI: 10.1017/thg.2012.6] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The human electroencephalogram (EEG) consists of oscillations that reflect the summation of postsynaptic potentials at the dendritic tree of cortical neurons. The strength of the oscillations (EEG power) is a highly genetic trait that has been related to individual differences in many phenotypes, including intelligence and liability for psychopathology. Here, we investigated whether brain anatomy underlies these EEG power differences by correlating it to gray and white matter volumes (GMV, WMV), and additionally investigated whether this association can be attributed to genes or environmental factors. EEG was measured in a sample of 405 young adult twins and their siblings, and power in the theta (~4 Hz), alpha (~10 Hz), and beta (~20 Hz) frequency bands determined. A subset of 121 subjects were also scanned in a 1.5 T MRI scanner, and gray and white matter volumes defined as the total of cortical and subcortical volumes, excluding cerebellum. Both MRI-based volumes and EEG power spectra were highly heritable. GMV and WMV correlated .25 to .29 with EEG power for the slower oscillations (theta, alpha). Moreover, these phenotypic correlations largely reflected genetic covariation, irrespective of oscillation frequency and volume type. Genetic correlations (.31 < rA < .43) revealed that only moderate proportions of the heritable variance overlapped between MRI volumes and EEG power. The results suggest that MRI volumes and EEG power share genetic sources of variation, which may reflect such processes as myelination, synaptic density, and dendritic outgrowth.
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Abstract
Aging is associated with a deterioration of daily (circadian) rhythms in physiology and behavior. Deficits in the function of the central circadian pacemaker in the suprachiasmatic nucleus (SCN) have been implicated, but the responsible mechanisms have not been clearly delineated. In this report, we characterize the progression of rhythm deterioration in mice to 900 d of age. Longitudinal behavioral and sleep-wake recordings in up to 30-month-old mice showed strong fragmentation of rhythms, starting at the age of 700 d. Patch-clamp recordings in this age group revealed deficits in membrane properties and GABAergic postsynaptic current amplitude. A selective loss of circadian modulation of fast delayed-rectifier and A-type K+ currents was observed. At the tissue level, phase synchrony of SCN neurons was grossly disturbed, with some subpopulations peaking in anti-phase and a reduction in amplitude of the overall multiunit activity rhythm. We propose that aberrant SCN rhythmicity in old animals--with electrophysiological arrhythmia at the single-cell level and phase desynchronization at the network level--can account for defective circadian function with aging.
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Stam C, van Straaten E. The organization of physiological brain networks. Clin Neurophysiol 2012; 123:1067-87. [PMID: 22356937 DOI: 10.1016/j.clinph.2012.01.011] [Citation(s) in RCA: 359] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2011] [Revised: 01/12/2012] [Accepted: 01/15/2012] [Indexed: 01/08/2023]
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50
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Smit DJA, Boersma M, Schnack HG, Micheloyannis S, Boomsma DI, Hulshoff Pol HE, Stam CJ, de Geus EJC. The brain matures with stronger functional connectivity and decreased randomness of its network. PLoS One 2012; 7:e36896. [PMID: 22615837 PMCID: PMC3352942 DOI: 10.1371/journal.pone.0036896] [Citation(s) in RCA: 98] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Accepted: 04/09/2012] [Indexed: 11/19/2022] Open
Abstract
We investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years) by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz' (1998) graph parameters C (local clustering) and L (global path length) for alpha (~10 Hz), beta (~20 Hz), and theta (~4 Hz) oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ~50 yrs). Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ~18 yrs). Older age (55+) was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05), while path length was related to both white matter (alpha: max. r = 38, p<001) and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001) volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain.
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Affiliation(s)
- Dirk J A Smit
- Biological Psychology, VU University, Amsterdam, The Netherlands.
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