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Gordillo D, Ramos da Cruz J, Moreno D, Garobbio S, Herzog MH. Do we really measure what we think we are measuring? iScience 2023; 26:106017. [PMID: 36844457 PMCID: PMC9947309 DOI: 10.1016/j.isci.2023.106017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/18/2022] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Tests used in the empirical sciences are often (implicitly) assumed to be representative of a given research question in the sense that similar tests should lead to similar results. Here, we show that this assumption is not always valid. We illustrate our argument with the example of resting-state electroencephalogram (EEG). We used multiple analysis methods, contrary to typical EEG studies where one analysis method is used. We found, first, that many EEG features correlated significantly with cognitive tasks. However, these EEG features correlated weakly with each other. Similarly, in a second analysis, we found that many EEG features were significantly different in older compared to younger participants. When we compared these EEG features pairwise, we did not find strong correlations. In addition, EEG features predicted cognitive tasks poorly as shown by cross-validated regression analysis. We discuss several explanations of these results.
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Affiliation(s)
- Dario Gordillo
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Corresponding author
| | - Janir Ramos da Cruz
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Institute for Systems and Robotics – Lisboa (LARSyS), Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
- Wyss Center for Bio and Neuroengineering, CH-1202 Geneva, Switzerland
| | - Dana Moreno
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Simona Garobbio
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Michael H. Herzog
- Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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Adamovich T, Zakharov I, Tabueva A, Malykh S. The thresholding problem and variability in the EEG graph network parameters. Sci Rep 2022; 12:18659. [PMID: 36333413 PMCID: PMC9636266 DOI: 10.1038/s41598-022-22079-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.
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Affiliation(s)
- Timofey Adamovich
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Ilya Zakharov
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Anna Tabueva
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Sergey Malykh
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
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Lum JAG, Clark GM, Bigelow FJ, Enticott PG. Resting state electroencephalography (EEG) correlates with children's language skills: Evidence from sentence repetition. BRAIN AND LANGUAGE 2022; 230:105137. [PMID: 35576738 DOI: 10.1016/j.bandl.2022.105137] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 05/02/2022] [Accepted: 05/02/2022] [Indexed: 06/15/2023]
Abstract
Spontaneous neural oscillatory activity reflects the brain's functional architecture and has previously been shown to correlate with perceptual, motor and executive skills. The current study used resting state electroencephalography to examine the relationship between spontaneous neural oscillatory activity and children's language skills. Participants in the study were 52 English-speaking children aged around 10-years. Language was assessed using a sentence repetition task. The main analysis revealed resting state theta power negatively correlated with this task. No significant correlations were found in the other studied frequency bands (delta, alpha, beta, gamma). As part of typical brain development, spontaneous theta power declines across childhood and adolescence. The negative correlation observed in this study may therefore be indicating children's language skills are related to the maturation of theta oscillations. More generally, the study provides further evidence that oscillatory activity in the developing brain, even at rest, is reliably associated with children's language skills.
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Affiliation(s)
- Jarrad A G Lum
- School of Psychology, Cognitive Neuroscience Unit, Deakin University, Geelong, Australia.
| | - Gillian M Clark
- School of Psychology, Cognitive Neuroscience Unit, Deakin University, Geelong, Australia
| | - Felicity J Bigelow
- School of Psychology, Cognitive Neuroscience Unit, Deakin University, Geelong, Australia
| | - Peter G Enticott
- School of Psychology, Cognitive Neuroscience Unit, Deakin University, Geelong, Australia
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Chow R, Rabi R, Paracha S, Hasher L, Anderson CPsych ND, Alain C. Default mode network and neural phase synchronization in healthy aging: A resting state EEG study. Neuroscience 2022; 485:116-128. [PMID: 35051530 DOI: 10.1016/j.neuroscience.2022.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 01/07/2022] [Accepted: 01/10/2022] [Indexed: 01/23/2023]
Abstract
Aging is associated with altered brain connectivity within the default mode network (DMN). Although research using functional magnetic resonance imaging has quantified age-related alterations in functional connectivity within this network during resting state, it is less clear how this may be reflected in electrophysiological measures, and how this relates to cognitive performance in older adults. The aim of this study was to quantify age differences in phase synchrony of the DMN during resting state, with particular focus on connectivity between the anterior node (i.e., medial prefrontal cortex, or mPFC) and other associated regions in this network. Electroencephalography was recorded from 55 younger adults (18-30 years, 28 females) and 34 older adults (64-88 years, 16 females) in two resting state conditions (eyes-open and -closed). Source-level functional connectivity was quantified using phase-locking value (PLV) with a spatial filter of six sources of interest, and were subjected to data-driven permutation testing between groups from 1 to 50 Hz. Older adults also completed tests of memory, language, executive functioning, and processing speed. Findings indicated decreased connectivity in the alpha2 range for older than younger adults between the mPFC and other DMN regions including the left angular gyrus and bilateral lateral temporal cortices, the latter of which were associated with lower performance in semantic fluency and executive functioning in older adults. Furthermore, greater PLV in theta and beta bands between the mPFC and posterior cingulate regions was found in older than younger adults. These results suggest age-related changes in DMN functional connectivity are non-uniform and frequency-dependent, and may reflect poorer performance in cognitive domains thought to decline with aging.
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Affiliation(s)
- Ricky Chow
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada
| | - Rahel Rabi
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada
| | - Shahier Paracha
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada
| | - Lynn Hasher
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada; Department of Psychology, University of Toronto, Ontario M5S 3G3, Canada
| | - Nicole D Anderson CPsych
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada; Department of Psychology, University of Toronto, Ontario M5S 3G3, Canada; Department of Psychiatry, University of Toronto, Ontario M5T 1R8, Canada
| | - Claude Alain
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario M6A 2E1, Canada; Department of Psychology, University of Toronto, Ontario M5S 3G3, Canada; Institute of Medical Sciences, University of Toronto, Ontario M5S 1A8, Canada.
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Ramos-Escobar N, Segura E, Olivé G, Rodriguez-Fornells A, François C. Oscillatory activity and EEG phase synchrony of concurrent word segmentation and meaning-mapping in 9-year-old children. Dev Cogn Neurosci 2021; 51:101010. [PMID: 34461393 PMCID: PMC8403737 DOI: 10.1016/j.dcn.2021.101010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 10/28/2022] Open
Abstract
When learning a new language, one must segment words from continuous speech and associate them with meanings. These complex processes can be boosted by attentional mechanisms triggered by multi-sensory information. Previous electrophysiological studies suggest that brain oscillations are sensitive to different hierarchical complexity levels of the input, making them a plausible neural substrate for speech parsing. Here, we investigated the functional role of brain oscillations during concurrent speech segmentation and meaning acquisition in sixty 9-year-old children. We collected EEG data during an audio-visual statistical learning task during which children were exposed to a learning condition with consistent word-picture associations and a random condition with inconsistent word-picture associations before being tested on their ability to recall words and word-picture associations. We capitalized on the brain dynamics to align neural activity to the same rate as an external rhythmic stimulus to explore modulations of neural synchronization and phase synchronization between electrodes during multi-sensory word learning. Results showed enhanced power at both word- and syllabic-rate and increased EEG phase synchronization between frontal and occipital regions in the learning compared to the random condition. These findings suggest that multi-sensory cueing and attentional mechanisms play an essential role in children's successful word learning.
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Affiliation(s)
- Neus Ramos-Escobar
- Dept. of Cognition, Development and Educational Science, Institute of Neuroscience, University of Barcelona, L'Hospitalet de Llobregat, Barcelona, 08097, Spain; Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 08097, Spain
| | - Emma Segura
- Dept. of Cognition, Development and Educational Science, Institute of Neuroscience, University of Barcelona, L'Hospitalet de Llobregat, Barcelona, 08097, Spain; Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 08097, Spain
| | - Guillem Olivé
- Dept. of Cognition, Development and Educational Science, Institute of Neuroscience, University of Barcelona, L'Hospitalet de Llobregat, Barcelona, 08097, Spain; Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 08097, Spain
| | - Antoni Rodriguez-Fornells
- Dept. of Cognition, Development and Educational Science, Institute of Neuroscience, University of Barcelona, L'Hospitalet de Llobregat, Barcelona, 08097, Spain; Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 08097, Spain; Catalan Institution for Research and Advanced Studies, ICREA, Barcelona, Spain.
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Is there a “g-neuron”? Establishing a systematic link between general intelligence (g) and the von Economo neuron. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101540] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Feklicheva I, Zakharov I, Chipeeva N, Maslennikova E, Korobova S, Adamovich T, Ismatullina V, Malykh S. Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity. Brain Sci 2021; 11:94. [PMID: 33450902 PMCID: PMC7828310 DOI: 10.3390/brainsci11010094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/04/2021] [Accepted: 01/11/2021] [Indexed: 11/17/2022] Open
Abstract
The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven's total scores and E series), and the ability to operate with verbal information (the "Conclusions" verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven's total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.
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Affiliation(s)
- Inna Feklicheva
- Laboratory of Molecular Genetic Research of Human Health and Development, Scientific and Educational Center “Biomedical Technologies”, Higher Medical and Biological School, South Ural State University, 454080 Chelyabinsk, Russia; (N.C.); (S.K.)
| | - Ilya Zakharov
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
| | - Nadezda Chipeeva
- Laboratory of Molecular Genetic Research of Human Health and Development, Scientific and Educational Center “Biomedical Technologies”, Higher Medical and Biological School, South Ural State University, 454080 Chelyabinsk, Russia; (N.C.); (S.K.)
| | - Ekaterina Maslennikova
- Center of Interdisciplinary Research in Education, Russian Academy of Education, 199121 Moscow, Russia;
| | - Svetlana Korobova
- Laboratory of Molecular Genetic Research of Human Health and Development, Scientific and Educational Center “Biomedical Technologies”, Higher Medical and Biological School, South Ural State University, 454080 Chelyabinsk, Russia; (N.C.); (S.K.)
| | - Timofey Adamovich
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
| | - Victoria Ismatullina
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
| | - Sergey Malykh
- Developmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, 125009 Moscow, Russia; (I.Z.); (T.A.); (V.I.); (S.M.)
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Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior. eNeuro 2020; 7:ENEURO.0101-20.2020. [PMID: 32826259 PMCID: PMC7484267 DOI: 10.1523/eneuro.0101-20.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/17/2020] [Accepted: 07/22/2020] [Indexed: 12/18/2022] Open
Abstract
The resting human brain exhibits spontaneous patterns of activity that reflect features of the underlying neural substrate. Examination of interareal coupling of resting-state oscillatory activity has revealed that the brain’s resting activity is composed of functional networks, whose topographies differ depending on oscillatory frequency, suggesting a role for carrier frequency as a means of creating multiplexed, or functionally segregated, communication channels between brain areas. Using canonical correlation analysis (CCA), we examined spectrally resolved resting-state connectivity patterns derived from magnetoencephalography (MEG) recordings to determine the relationship between connectivity intrinsic to different frequency channels and a battery of over a hundred behavioral and demographic indicators, in a group of 89 young healthy participants. We demonstrate that each of the classical frequency bands in the range 1–40 Hz (δ, θ, α, β, and γ) delineates a subnetwork that is behaviorally relevant, spatially distinct, and whose expression is either negatively or positively predictive of individual traits, with the strongest link in the α-band being negative and networks oscillating at different frequencies, such as θ, β, and γ carrying positive function.
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