1
|
Guenther S, Kosmyna N, Maes P. Image classification and reconstruction from low-density EEG. Sci Rep 2024; 14:16436. [PMID: 39013929 PMCID: PMC11252274 DOI: 10.1038/s41598-024-66228-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/28/2024] [Indexed: 07/18/2024] Open
Abstract
Recent advances in visual decoding have enabled the classification and reconstruction of perceived images from the brain. However, previous approaches have predominantly relied on stationary, costly equipment like fMRI or high-density EEG, limiting the real-world availability and applicability of such projects. Additionally, several EEG-based paradigms have utilized artifactual, rather than stimulus-related information yielding flawed classification and reconstruction results. Our goal was to reduce the cost of the decoding paradigm, while increasing its flexibility. Therefore, we investigated whether the classification of an image category and the reconstruction of the image itself is possible from the visually evoked brain activity measured by a portable, 8-channel EEG. To compensate for the low electrode count and to avoid flawed predictions, we designed a theory-guided EEG setup and created a new experiment to obtain a dataset from 9 subjects. We compared five contemporary classification models with our setup reaching an average accuracy of 34.4% for 20 image classes on hold-out test recordings. For the reconstruction, the top-performing model was used as an EEG-encoder which was combined with a pretrained latent diffusion model via double-conditioning. After fine-tuning, we reconstructed images from the test set with a 1000 trial 50-class top-1 accuracy of 35.3%. While not reaching the same performance as MRI-based paradigms on unseen stimuli, our approach greatly improved the affordability and mobility of the visual decoding technology.
Collapse
Affiliation(s)
- Sven Guenther
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
| | - Nataliya Kosmyna
- Media Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Pattie Maes
- Media Lab, Massachusetts Institute of Technology, Cambridge, USA
| |
Collapse
|
2
|
Tarailis P, Šimkutė D, Griškova-Bulanova I. Global Functional Connectivity is Associated with Mind Wandering Domain of Comfort. Brain Topogr 2024:10.1007/s10548-024-01042-6. [PMID: 38430284 DOI: 10.1007/s10548-024-01042-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 02/16/2024] [Indexed: 03/03/2024]
Abstract
The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. To assess the relationship between brain activity and subjective experiences, various questionnaires are used. Previous studies using Amsterdam Resting State Questionnaire were focusing on fMRI functional connectivity or EEG microstates and spectral aspect. Here, we utilized Global Field Synchronization as the parameter to estimate global functional connectivity. By re-analyzing the resting-state data from 226 young healthy participants we showed a strong evidence of relationship between ARSQ domain of Comfort and GFS values in the alpha range (r = 0.210, BF10 = 12.338) and substantial evidence for positive relationship between ARSQ domain of Comfort and GFS in the beta frequency range (r = 196, BF10 = 6.307). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.
Collapse
Affiliation(s)
- Povilas Tarailis
- Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland
- Life Sciences Center, Institute of Biosciences, Vilnius University, Sauletekio Ave. 7, Vilnius, LT-10257, Lithuania
| | - Dovilė Šimkutė
- Life Sciences Center, Institute of Biosciences, Vilnius University, Sauletekio Ave. 7, Vilnius, LT-10257, Lithuania
| | - Inga Griškova-Bulanova
- Life Sciences Center, Institute of Biosciences, Vilnius University, Sauletekio Ave. 7, Vilnius, LT-10257, Lithuania.
| |
Collapse
|
3
|
Wilkinson CL, Pierce LJ, Sideridis G, Wade M, Nelson CA. Associations between EEG trajectories, family income, and cognitive abilities over the first two years of life. Dev Cogn Neurosci 2023; 61:101260. [PMID: 37262938 PMCID: PMC10245106 DOI: 10.1016/j.dcn.2023.101260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/23/2023] [Accepted: 05/26/2023] [Indexed: 06/03/2023] Open
Abstract
We sought to characterize developmental trajectories of EEG spectral power over the first 2 years after birth and examine whether family income or maternal education alter those trajectories. We analyzed EEGs (n = 161 infants, 534 EEGs) collected longitudinally between 2 and 24 months of age, and calculated frontal absolute power across 7 canonical frequency bands. For each frequency band, a piecewise growth curve model was fit, resulting in an estimated intercept and two slope parameters from 2 to 9 months and 9-24 months of age. Across 6/7 frequency bands, absolute power significantly increased over age, with steeper slopes in the 2-9 month period compared to 9-24 months. Increased family income, but not maternal education, was associated with higher intercept (2-3 month power) across delta-gamma bands (p range = 0.002-0.04), and reduced change in power between 2 and 9 months of age in lower frequency bands (delta-alpha, p range = 0.01-0.02). There was no significant effect of income on slope between 9 and 24 months. EEG intercept and slope measures did not mediate relationships between income and 24-month verbal and nonverbal development. These results add to growing literature concerning the role of socioeconomic factors in shaping brain trajectories.
Collapse
Affiliation(s)
- Carol L Wilkinson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
| | - Lara J Pierce
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Psychology, York University, Toronto, Ontario, Canada
| | - Georgios Sideridis
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mark Wade
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Applied Psychology and Human Development, University of Toronto, Toronto, Ontario, Canada
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Harvard Graduate School of Education, Cambridge, MA 02138, USA
| |
Collapse
|
4
|
Malone SM, Harper J, Iacono WG. Longitudinal stability and change in time-frequency measures from an oddball task during adolescence and early adulthood. Psychophysiology 2023; 60:e14200. [PMID: 36281995 PMCID: PMC9868093 DOI: 10.1111/psyp.14200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 09/24/2022] [Accepted: 10/05/2022] [Indexed: 01/26/2023]
Abstract
Time-frequency representations of electroencephalographic signals lend themselves to a granular analysis of cognitive and psychological processes. Characterizing developmental trajectories of time-frequency measures can thus inform us about the development of the processes involved as well as correlated traits and behaviors. We decomposed electroencephalographic (EEG) activity in a large sample of individuals (N = 1692; 917 females), assessed at approximately 3-year intervals from the age of 11 to their mid-20s. Participants completed an oddball task that elicits a robust P3 response. Principal component analysis served to identify the primary dimensions of time-frequency energy. Component loadings were virtually identical across assessment waves. A common and stable set of time-frequency dynamics thus characterized EEG activity throughout this age range. Trajectories of changes in component scores suggest that aspects of brain development reflected in these components comprise two distinct phases, with marked decreases in component amplitude throughout much of adolescence followed by smaller yet significant rates of decreases into early adulthood. Although the structure of time-frequency activity was stable throughout adolescence and early adulthood, we observed subtle change in component loadings as well. Our findings suggest that striking developmental change in event-related potentials emerges through a gradual change in the magnitude and timing of a stable set of dimensions of time-frequency activity, illustrating the usefulness of time-frequency representations of EEG signals and longitudinal designs for understanding brain development. In addition, we provide proof of concept that trajectories of time-frequency activity can serve as potential endophenotypes for childhood externalizing psychopathology and alcohol use in adolescence and early adulthood.
Collapse
Affiliation(s)
- Stephen M. Malone
- Department of Psychology, University of Minnesota – Twin Cities, 75 East River Road Minneapolis, MN 55455, USA
| | - Jeremy Harper
- Department of Psychiatry and Behavioral Sciences, University of Minnesota – Twin Cities, 2450 Riverside Avenue South, F282/2A West Building Minneapolis, MN 55454, USA
| | - William G. Iacono
- Department of Psychology, University of Minnesota – Twin Cities, 75 East River Road Minneapolis, MN 55455, USA
| |
Collapse
|
5
|
Harjunen VJ, Sjö P, Ahmed I, Saarinen A, Farmer H, Salminen M, Järvelä S, Ruonala A, Jacucci G, Ravaja N. Increasing Self-Other Similarity Modulates Ethnic Bias in Sensorimotor Resonance to Others' Pain. Soc Cogn Affect Neurosci 2021; 17:673-682. [PMID: 34669949 PMCID: PMC9250302 DOI: 10.1093/scan/nsab113] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 10/01/2021] [Accepted: 10/20/2021] [Indexed: 11/15/2022] Open
Abstract
The tendency to simulate the pain of others within our own sensorimotor systems is a vital component of empathy. However, this sensorimotor resonance is modulated by a multitude of social factors including similarity in bodily appearance, e.g. skin colour. The current study investigated whether increasing self-other similarity via virtual transfer to another colour body reduced ingroup bias in sensorimotor resonance. A sample of 58 white participants was momentarily transferred to either a black or a white body using virtual reality technology. We then employed electroencephalography (EEG) to examine event-related desynchronization (ERD) in the sensorimotor beta (13-23 Hz) oscillations while they viewed black, white, and violet photorealistic virtual agents being touched with a noxious or soft object. While the noxious treatment of a violet agent did not increase beta ERD, amplified beta ERD in response to black agent's noxious vs. soft treatment was found in perceivers transferred to black body. Transfer to the white body dismissed the effect. Further exploratory analysis implied that the pain-related beta ERD occurred only when the agent and the participant were of the same colour. The results suggest that even short-lasting changes in bodily resemblance can modulate sensorimotor resonance to others' perceived pain.
Collapse
Affiliation(s)
- Ville Johannes Harjunen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Petja Sjö
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Imtiaj Ahmed
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Aino Saarinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Harry Farmer
- School of Human Sciences, University of Greenwich, London, United Kingdom.,Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Mikko Salminen
- Gamification Group, Faculty of Information Technology and Communications, Tampere University, Tampere, Finland
| | - Simo Järvelä
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Gamification Group, Faculty of Information Technology and Communications, Tampere University, Tampere, Finland
| | - Antti Ruonala
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Giulio Jacucci
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Niklas Ravaja
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| |
Collapse
|
6
|
Perone S, Anderson AJ, Zelazo PD. The influence of parental guidance on video game performance, exploration, and cortical activity in 5-year-old children. COGNITIVE DEVELOPMENT 2021. [DOI: 10.1016/j.cogdev.2021.101126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
7
|
Kamrud A, Borghetti B, Schubert Kabban C. The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models. SENSORS (BASEL, SWITZERLAND) 2021; 21:3225. [PMID: 34066595 PMCID: PMC8125354 DOI: 10.3390/s21093225] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 04/28/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper dataset partitioning and the resulting improper training, validation, and testing of a cross-participant model leads to overestimated model accuracy. We demonstrate this mathematically, and empirically, using five publicly available datasets. To build the cross-participant models for these datasets, we replicate published results and demonstrate how the model accuracies are significantly reduced when proper EEG cross-participant model guidelines are followed. Our empirical results show that by not following these guidelines, error rates of cross-participant models can be underestimated between 35% and 3900%. This misrepresentation of model performance for the general population potentially slows scientific progress toward truly high-performing classification models.
Collapse
Affiliation(s)
- Alexander Kamrud
- Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA; (B.B.); (C.S.K.)
| | | | | |
Collapse
|
8
|
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: 0.7] [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.
Collapse
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
| |
Collapse
|
9
|
Wriessnegger SC, Müller-Putz GR, Brunner C, Sburlea AI. Inter- and Intra-individual Variability in Brain Oscillations During Sports Motor Imagery. Front Hum Neurosci 2020; 14:576241. [PMID: 33192406 PMCID: PMC7662155 DOI: 10.3389/fnhum.2020.576241] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/24/2020] [Indexed: 11/23/2022] Open
Abstract
The aim of this work was to re-evaluate electrophysiological data from a previous study on motor imagery (MI) with a special focus on observed inter- and intra-individual differences. More concretely, we investigated event-related desynchronization/synchronization patterns during sports MI (playing tennis) compared with simple MI (squeezing a ball) and discovered high variability across participants. Thirty healthy volunteers were divided in two groups; the experimental group (EG) performed a physical exercise between two imagery sessions, and the control group (CG) watched a landscape movie without physical activity. We computed inter-individual differences by assessing the dissimilarities among subjects for each group, condition, time period, and frequency band. In the alpha band, we observe some clustering in the ranking of the subjects, therefore showing smaller distances than others. Moreover, in our statistical evaluation, we observed a consistency in ranking across time periods both for the EG and for the CG. For the latter, we also observed similar rankings across conditions. On the contrary, in the beta band, the ranking of the subjects was more similar for the EG across conditions and time periods than for the subjects of the CG. With this study, we would like to draw attention to variability measures instead of primarily focusing on the identification of common patterns across participants, which often do not reflect the whole neurophysiological reality.
Collapse
Affiliation(s)
- Selina C Wriessnegger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | | | - Andreea I Sburlea
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| |
Collapse
|
10
|
Li M, Lin F, Xu G. A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold. Int J Neural Syst 2020; 30:2050009. [PMID: 32116091 DOI: 10.1142/s0129065720500094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.
Collapse
Affiliation(s)
- Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
| | - Fang Lin
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
| |
Collapse
|
11
|
A mathematical model of the interaction between bottom-up and top-down attention controllers in response to a target and a distractor in human beings. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2019.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
12
|
Ujma PP, Konrad BN, Simor P, Gombos F, Körmendi J, Steiger A, Dresler M, Bódizs R. Sleep EEG functional connectivity varies with age and sex, but not general intelligence. Neurobiol Aging 2019; 78:87-97. [DOI: 10.1016/j.neurobiolaging.2019.02.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 01/14/2019] [Accepted: 02/10/2019] [Indexed: 11/16/2022]
|
13
|
Cox R, Schapiro AC, Stickgold R. Variability and stability of large-scale cortical oscillation patterns. Netw Neurosci 2018; 2:481-512. [PMID: 30320295 PMCID: PMC6175693 DOI: 10.1162/netn_a_00046] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/26/2018] [Indexed: 11/08/2022] Open
Abstract
Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior. Neural oscillations are critical for the human brain’s ability to optimally respond to complex environmental input. However, relatively little is known about the network properties of these oscillatory rhythms. We used electroencephalography (EEG) to analyze large-scale brain wave patterns, focusing on multiple frequency bands and several key features of oscillatory communication. We show that networks defined in this manner are, in fact, distinct, suggesting that EEG activity encompasses multiple, parallel information processing streams. Remarkably, the same networks can be used to uniquely identify individuals over a period of approximately half a year, thus serving as neural fingerprints. These findings indicate that investigating oscillatory dynamics from a network perspective holds considerable promise as a tool to understand human cognition and behavior.
Collapse
Affiliation(s)
- Roy Cox
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, USA
| | - Anna C Schapiro
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, USA
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, USA
| |
Collapse
|
14
|
Fraga González G, Smit DJA, van der Molen MJW, Tijms J, Stam CJ, de Geus EJC, van der Molen MW. EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis. Front Hum Neurosci 2018; 12:341. [PMID: 30214403 PMCID: PMC6125304 DOI: 10.3389/fnhum.2018.00341] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 08/10/2018] [Indexed: 11/13/2022] Open
Abstract
Developmental dyslexia may involve deficits in functional connectivity across widespread brain networks that enable fluent reading. We investigated the large-scale organization of electroencephalography (EEG) functional networks at rest in 28 dyslexics and 36 typically reading adults. For each frequency band (delta, theta alpha and beta), we assessed functional connectivity strength with the phase lag index (PLI). Network topology was examined using minimum spanning tree (MST) graphs derived from the functional connectivity matrices. We found significant group differences in the alpha band (8-13 Hz). The graph analysis indicated more interconnected nodes, in dyslexics compared to typical readers. The graph metrics were significantly correlated with age in dyslexics but not in typical readers, which may indicate more heterogeneity in maturation of brain networks in dyslexics. The present findings support the involvement of alpha oscillations in higher cognition and the sensitivity of graph metrics to characterize functional networks in adult dyslexia. Finally, the current results extend our previous findings on children.
Collapse
Affiliation(s)
- Gorka Fraga González
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.,Rudolf Berlin Center, Amsterdam, Netherlands
| | - Dirk J A Smit
- Department of Biological Psychology, VU University, Amsterdam, Netherlands.,Neuroscience Campus Amsterdam, VU University, Amsterdam, Netherlands
| | - Melle J W van der Molen
- Institute of Psychology, Leiden University, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
| | - Jurgen Tijms
- Rudolf Berlin Center, Amsterdam, Netherlands.,IWAL Institute, Amsterdam, Netherlands
| | - Cornelis Jan Stam
- Department of Clinical Neuropsychology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, Netherlands.,Neuroscience Campus Amsterdam, VU University, Amsterdam, Netherlands
| | - Maurits W van der Molen
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
15
|
Sobkow A, Traczyk J, Kaufman SB, Nosal C. The structure of intuitive abilities and their relationships with intelligence and Openness to Experience. INTELLIGENCE 2018. [DOI: 10.1016/j.intell.2017.12.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
16
|
Jang YE, Jeong SA, Kim SY, Song IK, Lee JH, Kim JT, Kim HS. The Efficacy of Intraoperative EEG to Predict the Occurrence of Emergence Agitation in the Postanesthetic Room After Sevoflurane Anesthesia in Children. J Perianesth Nurs 2018; 33:45-52. [PMID: 29362046 DOI: 10.1016/j.jopan.2015.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 10/02/2015] [Accepted: 10/03/2015] [Indexed: 11/28/2022]
Abstract
PURPOSE Emergence agitation (EA) is common after sevoflurane anesthesia, but there are no definite predictors. This study investigated whether intraoperative electroencephalography (EEG) can indicate the occurrence of EA in children. DESIGN A prospective predictive study design was used. METHODS EEG-derived parameters (spectral edge frequency 95, beta, alpha, theta, and delta power) were measured at 1.0 minimum alveolar concentration (MAC) and 0.3 MAC of end-tidal sevoflurane (EtSEVO) in 29 patients. EA was evaluated using an EA score (EAS) in the postanesthetic care unit on arrival (EAS 0) and at 15 and 30 minutes after arrival (EAS 15 and EAS 30). The correlation between EEG-derived parameters and EAS was analyzed using Spearman correlation, and receiver-operating characteristic curve analysis was used to measure the predictability. FINDINGS EA occurred in 11 patients. The alpha power at 1.0 MAC of EtSEVO was correlated with EAS 15 and EAS 30. The theta/alpha ratio at 0.3 MAC of EtSEVO was correlated with EAS 30. The area under the receiver-operating characteristic curve of percentage of alpha bands at 0.3 MAC of EtSEVO and the occurrence of EA was 0.672. CONCLUSIONS Children showing high-alpha powers and low theta powers (= low theta/alpha ratio) during emergence from sevoflurane anesthesia are at high risk of EA in the postanesthetic care unit.
Collapse
|
17
|
Renauld E, Descoteaux M, Bernier M, Garyfallidis E, Whittingstall K. Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves. PLoS One 2016; 11:e0156436. [PMID: 27383146 PMCID: PMC4934857 DOI: 10.1371/journal.pone.0156436] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 05/13/2016] [Indexed: 12/13/2022] Open
Abstract
At rest, healthy human brain activity is characterized by large electroencephalography (EEG) fluctuations in the 8-13 Hz range, commonly referred to as the alpha band. Although it is well known that EEG alpha activity varies across individuals, few studies have investigated how this may be related to underlying morphological variations in brain structure. Specifically, it is generally believed that the lateral geniculate nucleus (LGN) and its efferent fibres (optic radiation, OR) play a key role in alpha activity, yet it is unclear whether their shape or size variations contribute to its inter-subject variability. Given the widespread use of EEG alpha in basic and clinical research, addressing this is important, though difficult given the problems associated with reliably segmenting the LGN and OR. For this, we employed a multi-modal approach and combined diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI) and EEG in 20 healthy subjects to measure structure and function, respectively. For the former, we developed a new, semi-automated approach for segmenting the OR and LGN, from which we extracted several structural metrics such as volume, position and diffusivity. Although these measures corresponded well with known morphology based on previous post-mortem studies, we nonetheless found that their inter-subject variability was not significantly correlated to alpha power or peak frequency (p >0.05). Our results therefore suggest that alpha variability may be mediated by an alternative structural source and our proposed methodology may in general help in better understanding the influence of anatomy on function such as measured by EEG or fMRI.
Collapse
Affiliation(s)
- Emmanuelle Renauld
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, Qc, Canada
- Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, Qc, Canada
- Centre d’Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche du CHUS, Sherbrooke, Qc, Canada
| | - Michaël Bernier
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
| | - Eleftherios Garyfallidis
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, Qc, Canada
| | - Kevin Whittingstall
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
- Department of Diagnostic Radiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
- Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, Qc, Canada
- Centre d’Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche du CHUS, Sherbrooke, Qc, Canada
| |
Collapse
|
18
|
The genetic architecture of correlations between perceptual timing, motor timing, and intelligence. INTELLIGENCE 2016. [DOI: 10.1016/j.intell.2016.04.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
19
|
Prat CS, Yamasaki BL, Kluender RA, Stocco A. Resting-state qEEG predicts rate of second language learning in adults. BRAIN AND LANGUAGE 2016; 157-158:44-50. [PMID: 27164483 DOI: 10.1016/j.bandl.2016.04.007] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 03/18/2016] [Accepted: 04/10/2016] [Indexed: 06/05/2023]
Abstract
Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner.
Collapse
Affiliation(s)
- Chantel S Prat
- University of Washington, Department of Psychology and Institute for Learning & Brain Sciences, United States.
| | - Brianna L Yamasaki
- University of Washington, Department of Psychology and Institute for Learning & Brain Sciences, United States
| | - Reina A Kluender
- University of Washington, Department of Psychology and Institute for Learning & Brain Sciences, United States
| | - Andrea Stocco
- University of Washington, Department of Psychology and Institute for Learning & Brain Sciences, United States
| |
Collapse
|
20
|
Abstract
Sleep spindles are thalamocortical oscillations in nonrapid eye movement sleep, which play an important role in sleep-related neuroplasticity and offline information processing. Sleep spindle features are stable within and vary between individuals, with, for example, females having a higher number of spindles and higher spindle density than males. Sleep spindles have been associated with learning potential and intelligence; however, the details of this relationship have not been fully clarified yet. In a sample of 160 adult human subjects with a broad IQ range, we investigated the relationship between sleep spindle parameters and intelligence. In females, we found a positive age-corrected association between intelligence and fast sleep spindle amplitude in central and frontal derivations and a positive association between intelligence and slow sleep spindle duration in all except one derivation. In males, a negative association between intelligence and fast spindle density in posterior regions was found. Effects were continuous over the entire IQ range. Our results demonstrate that, although there is an association between sleep spindle parameters and intellectual performance, these effects are more modest than previously reported and mainly present in females. This supports the view that intelligence does not rely on a single neural framework, and stronger neural connectivity manifesting in increased thalamocortical oscillations in sleep is one particular mechanism typical for females but not males.
Collapse
|
21
|
Soh P, Narayanan B, Khadka S, Calhoun VD, Keshavan MS, Tamminga CA, Sweeney JA, Clementz BA, Pearlson GD. Joint Coupling of Awake EEG Frequency Activity and MRI Gray Matter Volumes in the Psychosis Dimension: A BSNIP Study. Front Psychiatry 2015; 6:162. [PMID: 26617533 PMCID: PMC4637406 DOI: 10.3389/fpsyt.2015.00162] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 10/26/2015] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Many studies have examined either electroencephalogram (EEG) frequency activity or gray matter volumes (GMV) in various psychoses [including schizophrenia (SZ), schizoaffective (SZA), and psychotic bipolar disorder (PBP)]. Prior work demonstrated similar EEG and gray matter abnormalities in both SZ and PBP. Integrating EEG and GMV and jointly analyzing the combined data fully elucidates the linkage between the two and may provide better biomarker- or endophenotype-specificity for a particular illness. Joint exploratory investigations of EEG and GMV are scarce in the literature and the relationship between the two in psychosis is even less explored. We investigated a joint multivariate model to test whether the linear relationship or linkage between awake EEG (AEEG) frequency activity and GMV is abnormal across the psychosis dimension and if such effects are also present in first-degree relatives. METHODS We assessed 607 subjects comprising 264 probands [105 SZ, 72 SZA, and 87 PBP], 233 of their first degree relatives [82 SZ relatives (SZR), 71 SZA relatives (SZAR), and 80 PBP relatives (PBPR)], and 110 healthy comparison subjects (HC). All subjects underwent structural MRI (sMRI) and EEG scans. Frequency activity and voxel-based morphometric GMV were derived from EEG and sMRI data, respectively. Seven AEEG frequency and gray matter components were extracted using Joint independent component analysis (jICA). The loading coefficients (LC) were examined for group differences using analysis of covariance. Further, the LCs were correlated with psychopathology scores to identify relationship with clinical symptoms. RESULTS Joint ICA revealed a single component differentiating SZ from HC (p < 0.006), comprising increased posterior alpha activity associated with decreased volume in inferior parietal lobe, supramarginal, parahippocampal gyrus, middle frontal, inferior temporal gyri, and increased volume of uncus and culmen. No components were aberrant in either PBP or SZA or any relative group. No significant association was identified with clinical symptom measures. CONCLUSION Our data suggest that a joint EEG and GMV model yielded a biomarker specific to SZ, not abnormal in PBP or SZA. Alpha activity was related to both increased and decreased volume in different cortical structures. Additionally, the joint model failed to identify endophenotypes across psychotic disorders.
Collapse
Affiliation(s)
- Pauline Soh
- Olin Neuropsychiatry Research Center, Institute of Living , Hartford, CT , USA
| | - Balaji Narayanan
- Olin Neuropsychiatry Research Center, Institute of Living , Hartford, CT , USA
| | - Sabin Khadka
- Olin Neuropsychiatry Research Center, Institute of Living , Hartford, CT , USA
| | - Vince D Calhoun
- Department of Electrical and Computer Engineering, University of New Mexico , Albuquerque, NM , USA ; The Mind Research Network , Albuquerque, NM , USA ; Department of Psychiatry, Yale University School of Medicine , New Haven, CT , USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School , Boston, MA , USA
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center , Dallas, TX , USA
| | - John A Sweeney
- Department of Psychiatry, University of Texas Southwestern Medical Center , Dallas, TX , USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia , Athens, GA , USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living , Hartford, CT , USA ; Department of Psychiatry, Yale University School of Medicine , New Haven, CT , USA ; Department of Neurobiology, Yale University School of Medicine , New Haven, CT , USA
| |
Collapse
|
22
|
Volf NV, Belousova LV, Knyazev GG, Kulikov AV. Gender differences in association between serotonin transporter gene polymorphism and resting-state EEG activity. Neuroscience 2014; 284:513-521. [PMID: 25450956 DOI: 10.1016/j.neuroscience.2014.10.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Revised: 09/24/2014] [Accepted: 10/17/2014] [Indexed: 12/16/2022]
Abstract
Human brain oscillations represent important features of information processing and are highly heritable. Gender has been observed to affect association between the 5-HTTLPR (serotonin-transporter-linked polymorphic region) polymorphism and various endophenotypes. This study aimed to investigate the effects of 5-HTTLPR on the spontaneous electroencephalography (EEG) activity in healthy male and female subjects. DNA samples extracted from buccal swabs and resting EEG recorded at 60 standard leads were collected from 210 (101 men and 109 women) volunteers. Spectral EEG power estimates and cortical sources of EEG activity were investigated. It was shown that effects of 5-HTTLPR polymorphism on electrical activity of the brain vary as a function of gender. Women with the S/L genotype had greater global EEG power compared to men with the same genotype. In men, current source density was markedly different among genotype groups in only alpha 2 and alpha 3 frequency ranges: S/S allele carriers had higher current source density estimates in the left inferior parietal lobule in comparison with the L/L group. In women, genotype difference in global power asymmetry was found in the central-temporal region. Contrasting L/L and S/L genotype carriers also yielded significant effects in the right hemisphere inferior parietal lobule and the right postcentral gyrus with L/L genotype carriers showing lower current source density estimates than S/L genotype carriers in all but gamma bands. So, in women, the effects of 5-HTTLPR polymorphism were associated with modulation of the EEG activity in a wide range of EEG frequencies. The significance of the results lies in the demonstration of gene by sex interaction with resting EEG that has implications for understanding sex-related differences in affective states, emotion and cognition.
Collapse
Affiliation(s)
- N V Volf
- State Research Institute of Physiology and Fundamental Medicine, Siberian Branch of the Russian Academy of Medical Sciences, Timakova Strasse 4, Novosibirsk 630117, Russia; Novosibirsk State University, Pirogova Strasse 2, Novosibirsk 630090, Russia.
| | - L V Belousova
- State Research Institute of Physiology and Fundamental Medicine, Siberian Branch of the Russian Academy of Medical Sciences, Timakova Strasse 4, Novosibirsk 630117, Russia.
| | - G G Knyazev
- State Research Institute of Physiology and Fundamental Medicine, Siberian Branch of the Russian Academy of Medical Sciences, Timakova Strasse 4, Novosibirsk 630117, Russia.
| | - A V Kulikov
- Novosibirsk State University, Pirogova Strasse 2, Novosibirsk 630090, Russia; Institute of Cytology and Genetics of Siberian Branch of Russian Academy of Sciences, Prospekt Lavrentyeva, 10, Novosibirsk 630090, Russia.
| |
Collapse
|
23
|
Associative learning alone is insufficient for the evolution and maintenance of the human mirror neuron system. Behav Brain Sci 2014; 37:212-3. [DOI: 10.1017/s0140525x13002422] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
AbstractCook et al. argue that mirror neurons originate from associative learning processes, without evolutionary influence from social-cognitive mechanisms. We disagree with this claim and present arguments based upon cross-species comparisons, EEG findings, and developmental neuroscience that the evolution of mirror neurons is most likely driven simultaneously and interactively by evolutionarily adaptive psychological mechanisms and lower-level biological mechanisms that support them.
Collapse
|
24
|
Affiliation(s)
- John Kounios
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19102;
| | - Mark Beeman
- Department of Psychology, Northwestern University, Evanston, Illinois 60208;
| |
Collapse
|
25
|
Grandy TH, Werkle-Bergner M, Chicherio C, Schmiedek F, Lövdén M, Lindenberger U. Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults. Psychophysiology 2013; 50:570-82. [PMID: 23551082 DOI: 10.1111/psyp.12043] [Citation(s) in RCA: 152] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2012] [Accepted: 02/21/2013] [Indexed: 11/29/2022]
Abstract
The individual alpha frequency (IAF) of the human EEG reflects systemic properties of the brain, is highly heritable, and relates to cognitive functioning. Not much is known about the modifiability of IAF by cognitive interventions. We report analyses of resting EEG from a large-scale training study in which healthy younger (20-31 years, N = 30) and older (65-80 years, N = 28) adults practiced 12 cognitive tasks for ∼100 1-h sessions. EEG was recorded before and after the cognitive training intervention. In both age groups, IAF (and, in a control analysis, alpha amplitude) did not change, despite large gains in cognitive performance. As within-session reliability and test-retest stability were high for both age groups, imprecise measurements cannot account for the findings. In sum, IAF is highly stable in healthy adults up to 80 years, not easily modifiable by cognitive interventions alone, and thus qualifies as a stable neurophysiological trait marker.
Collapse
Affiliation(s)
- Thomas H Grandy
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany
| | | | | | | | | | | |
Collapse
|
26
|
Willemsen G, Vink JM, Abdellaoui A, den Braber A, van Beek JHDA, Draisma HHM, van Dongen J, van 't Ent D, Geels LM, van Lien R, Ligthart L, Kattenberg M, Mbarek H, de Moor MHM, Neijts M, Pool R, Stroo N, Kluft C, Suchiman HED, Slagboom PE, de Geus EJC, Boomsma DI. The Adult Netherlands Twin Register: twenty-five years of survey and biological data collection. Twin Res Hum Genet 2013; 16:271-81. [PMID: 23298648 PMCID: PMC3739974 DOI: 10.1017/thg.2012.140] [Citation(s) in RCA: 159] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Over the past 25 years, the Adult Netherlands Twin Register (ANTR) has collected a wealth of information on physical and mental health, lifestyle, and personality in adolescents and adults. This article provides an overview of the sources of information available, the main research findings, and an outlook for the future. Between 1991 and 2012, longitudinal surveys were completed by twins, their parents, siblings, spouses, and offspring. Data are available for 33,957 participants, with most individuals having completed two or more surveys. Smaller projects provided in-depth phenotyping, including measurements of the autonomic nervous system, neurocognitive function, and brain imaging. For 46% of the ANTR participants, DNA samples are available and whole genome scans have been obtained in more than 11,000 individuals. These data have resulted in numerous studies on heritability, gene x environment interactions, and causality, as well as gene finding studies. In the future, these studies will continue with collection of additional phenotypes, such as metabolomic and telomere length data, and detailed genetic information provided by DNA and RNA sequencing. Record linkage to national registers will allow the study of morbidity and mortality, thus providing insight into the development of health, lifestyle, and behavior across the lifespan.
Collapse
Affiliation(s)
- Gonneke Willemsen
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
27
|
Abstract
This issue on the genetics of brain imaging phenotypes is a celebration of the happy marriage between two of science's highly interesting fields: neuroscience and genetics. The articles collected here are ample evidence that a good deal of synergy exists in this marriage. A wide selection of papers is presented that provide many different perspectives on how genes cause variation in brain structure and function, which in turn influence behavioral phenotypes (including psychopathology). They are examples of the many different methodologies in contemporary genetics and neuroscience research. Genetic methodology includes genome-wide association (GWA), candidate-gene association, and twin studies. Sources of data on brain phenotypes include cortical gray matter (GM) structural/volumetric measures from magnetic resonance imaging (MRI); white matter (WM) measures from diffusion tensor imaging (DTI), such as fractional anisotropy; functional- (activity-) based measures from electroencephalography (EEG), and functional MRI (fMRI). Together, they reflect a combination of scientific fields that have seen great technological advances, whether it is the single-nucleotide polymorphism (SNP) array in genetics, the increasingly high-resolution MRI imaging, or high angular resolution diffusion imaging technique for measuring WM connective properties.
Collapse
|