Theta/Beta Ratio Neurofeedback Effects on Resting and Task-Related Theta Activity in Children with ADHD
Status Publisher Jazyk angličtina Země Německo Médium print-electronic
Typ dokumentu časopisecké články
PubMed
39674997
DOI
10.1007/s10484-024-09675-w
PII: 10.1007/s10484-024-09675-w
Knihovny.cz E-zdroje
- Klíčová slova
- Children with ADHD, Error-related dynamics, Fm-theta, ICAN study, Task-related and resting state theta, Theta–beta ratio neurofeedback,
- Publikační typ
- časopisecké články MeSH
The EEG theta band displays distinct roles in resting and task states. Low resting theta and transient increases in frontal-midline (fm) theta power during tasks are associated with better cognitive control, such as error monitoring. ADHD can disrupt this balance, resulting in high resting theta linked to drowsiness and low fm-theta activity associated with reduced cognitive abilities. Theta/beta ratio (TBR) neurofeedback aims to normalize resting state activity by downregulating theta, which could potentially unfavorably affect task-related fm-theta. This study examines the TBR neurofeedback's impact on both resting and fm-theta activity, hypothesizing that remission depends on these effects. We analyzed data from a multi-center, double-blind randomized controlled trial with 142 children with ADHD and high TBR (ICAN study). Participants were randomized into experimental or sham NF groups. EEG measurements were taken at rest and during an Oddball task before and after neurofeedback, assessing global electrodes for resting theta and fm electrodes during error dynamics. Post-intervention changes were calculated as differences, and ANOVAs were conducted on GROUP, REMISSION, and CONDITION variables. Final analysis included fewer participants for all analyses. Resting state analysis showed no significant effects on global or fm-theta after TBR neurofeedback. Error dynamics analysis was inconclusive for global and fm-theta in both remitters and non-remitters. Results suggest that the current TBR neurofeedback protocol did not reduce aberrant resting state theta, and emphasize the need for refined protocols targeting specific theta-band networks to reduce resting-state theta without affecting fm-theta related to cognitive control.
Department of Psychiatry and Behavioral Health Ohio State University Columbus USA
Department of Psychology University of North Carolina at Asheville Asheville USA
Research Institute Brainclinics Brainclinics Foundation Nijmegen The Netherlands
Synaeda Research Synaeda Psycho Medisch Centrum Drachten The Netherlands
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Abramovitch, A., Short, T., & Schweiger, A. (2021). The C Factor: Cognitive dysfunction as a transdiagnostic dimension in psychopathology. Clinical Psychology Review, 86, 102007. https://doi.org/10.1016/j.cpr.2021.102007 PubMed DOI
Ahmadi, M., Kazemi, K., Kuc, K., Cybulska-Klosowicz, A., Zakrzewska, M., Racicka-Pawlukiewicz, E., Helfroush, M. S., & Aarabi, A. (2020). Cortical source analysis of resting state EEG data in children with attention deficit hyperactivity disorder. Clinical Neurophysiology, 131(9), 2115–2130. https://doi.org/10.1016/j.clinph.2020.05.028 PubMed DOI
Arnold, L. E., Arns, M., Barterian, J., Bergman, R., Black, S., Conners, C. K., Connor, S., Dasgupta, S., deBeus, R., Higgins, T., Hirshberg, L., Hollway, J. A., Kerson, C., Lightstone, H., Lofthouse, N., Lubar, J., McBurnett, K., Monastra, V., Buchan-Page, K., & Williams, C. E. (2021). Double-blind placebo-controlled randomized clinical trial of neurofeedback for attention-deficit/hyperactivity disorder with 13-month follow-up. Journal of the American Academy of Child & Adolescent Psychiatry, 60(7), 841–855. https://doi.org/10.1016/j.jaac.2020.07.906 DOI
Arns, M., Gunkelman, J., Breteler, M., & Spronk, D. (2008). EEG phenotypes predict treatment outcome to stimulants in children with ADHD. Journal of Integrative Neuroscience, 7(3), 421–438. https://doi.org/10.1142/s0219635208001897 PubMed DOI
Arns, M., Drinkenburg, W., & Kenemans, J. L. (2012). The effects of QEEG-informed neurofeedback in ADHD: An open-label pilot study. Applied Psychophysiology and Biofeedback, 37(3), 171–180. https://doi.org/10.1007/s10484-012-9191-4 PubMed DOI PMC
Arns, M., Conners, C. K., & Kraemer, H. C. (2013). A decade of EEG theta/beta ratio research in ADHD: A meta-analysis. Journal of Attention Disorders, 17(5), 374–383. https://doi.org/10.1177/1087054712460087 PubMed DOI
Balogh, L., & Czobor, P. (2016). Post-error slowing in patients with ADHD: A meta-analysis. Journal of Attention Disorders, 20(12), 1004–1016. https://doi.org/10.1177/1087054714528043 PubMed DOI
Barry, R. J., Clarke, A. R., & Johnstone, S. J. (2003). A review of electrophysiology in attention-deficit/hyperactivity disorder: I Qualitative and Quantitative Electroencephalography. Clinical Neurophysiology, 114(2), 171–183. https://doi.org/10.1016/S1388-2457(02)00362-0 PubMed DOI
Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (2018). How many trials does it take to get a significant ERP effect? It Depends. Psychophysiology, 55(6), e13049. https://doi.org/10.1111/psyp.13049 PubMed DOI
Bu, J., Young, K. D., Hong, W., Ma, R., Song, H., Wang, Y., Zhang, W., Hampson, M., Hendler, T., & Zhang, X. (2019). Effect of deactivation of activity patterns related to smoking cue reactivity on nicotine addiction. Brain: A Journal of Neurology, 142(6), 1827–1841. https://doi.org/10.1093/brain/awz114
Bussalb, A., Congedo, M., Barthelemy, Q., Ojeda, D., Acquaviva, E., Delorme, R., & Mayaud, L. (2019). Clinical and experimental factors influencing the efficacy of neurofeedback in ADHD: A meta-analysis. Frontiers in Psychiatry, 10, 35. https://doi.org/10.3389/fpsyt.2019.00035 PubMed DOI PMC
Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science (New York, N.Y.), 304(5679), 1926–1929. https://doi.org/10.1126/science.1099745
Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 3(3), 223–230. https://doi.org/10.1016/j.bpsc.2017.11.007 PubMed DOI
Cai, D., Deng, M., Yu, J., Nan, W., & Leung, A. W. S. (2021). The relationship of resting-state EEG oscillations to executive functions in middle childhood. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 164, 64–70. https://doi.org/10.1016/j.ijpsycho.2021.02.021 PubMed DOI
Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18(8), 414–421. https://doi.org/10.1016/j.tics.2014.04.012 PubMed DOI PMC
Cavanagh, J. F., & Shackman, A. J. (2015). Frontal midline theta reflects anxiety and cognitive control: Meta-analytic evidence. Journal of Physiology-Paris, 109(1), 3–15. https://doi.org/10.1016/j.jphysparis.2014.04.003 PubMed DOI
Caye, A., Rocha, T.B.-M., Anselmi, L., Murray, J., Menezes, A. M. B., Barros, F. C., Gonçalves, H., Wehrmeister, F., Jensen, C. M., Steinhausen, H.-C., Swanson, J. M., Kieling, C., & Rohde, L. A. (2016). Attention-deficit/hyperactivity disorder trajectories from childhood to young adulthood: Evidence from a birth cohort supporting a late-onset syndrome. JAMA Psychiatry, 73(7), 705–712. https://doi.org/10.1001/jamapsychiatry.2016.0383 PubMed DOI
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2. vyd.). Routledge. https://doi.org/10.4324/9780203771587
Cohen, M. X. (2011). Error-related medial frontal theta activity predicts cingulate-related structural connectivity. NeuroImage, 55(3), 1373–1383. https://doi.org/10.1016/j.neuroimage.2010.12.072 PubMed DOI
Cohen, M. X. (2014). A neural microcircuit for cognitive conflict detection and signaling. Trends in Neurosciences, 37(9), 480–490. https://doi.org/10.1016/j.tins.2014.06.004 PubMed DOI
Cooper, P. S., Karayanidis, F., McKewen, M., McLellan-Hall, S., Wong, A. S. W., Skippen, P., & Cavanagh, J. F. (2019). Frontal theta predicts specific cognitive control-induced behavioural changes beyond general reaction time slowing. NeuroImage, 189, 130–140. https://doi.org/10.1016/j.neuroimage.2019.01.022 PubMed DOI
Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M. P., & Castellanos, F. X. (2012). Toward systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies. American Journal of Psychiatry, 169(10), 1038–1055. https://doi.org/10.1176/appi.ajp.2012.11101521 PubMed DOI
Cury, C., Maurel, P., Gribonval, R., & Barillot, C. (2020). A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction. Frontiers in Neuroscience, 13, 1451. https://doi.org/10.3389/fnins.2019.01451 PubMed DOI PMC
Danielmeier, C., & Ullsperger, M. (2011). Post-error adjustments. Frontiers in Psychology, 2. https://doi.org/10.3389/fpsyg.2011.00233
Debnath, R., Tang, A., Zeanah, C. H., Nelson, C. A., & Fox, N. A. (2020). The long-term effects of institutional rearing, foster care intervention and disruptions in care on brain electrical activity in adolescence. Developmental Science, 23(1), e12872. https://doi.org/10.1111/desc.12872 PubMed DOI
Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009 PubMed DOI
Doppelmayr, M., Klimesch, W., Pachinger, T., & Ripper, B. (1998). Individual differences in brain dynamics: Important implications for the calculation of event-related band power. Biological Cybernetics, 79(1), 49–57. https://doi.org/10.1007/s004220050457 PubMed DOI
Enriquez-Geppert, S., Huster, R. J., Figge, C., & Herrmann, C. S. (2014). Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting. Frontiers in Behavioral Neuroscience, 8, 420. https://doi.org/10.3389/fnbeh.2014.00420 PubMed DOI PMC
Eschmann, K. C. J., & Mecklinger, A. (2022). Improving cognitive control: Is theta neurofeedback training associated with proactive rather than reactive control enhancement? Psychophysiology, 59(5), e13873. https://doi.org/10.1111/psyp.13873 PubMed DOI
Faller, J., Cummings, J., Saproo, S., & Sajda, P. (2018). Regulation of arousal via on-line neurofeedback improves human performance in a demanding sensory-motor task (s. 428755). https://doi.org/10.1101/428755
Food and Drug Administration. (2013). De novo classification request for neuropsychiatric EEG-based assessment aid for ADHD (NEBA) system. In: K112711.
Franke, B., Michelini, G., Asherson, P., Banaschewski, T., Bilbow, A., Buitelaar, J. K., Cormand, B., Faraone, S. V., Ginsberg, Y., Haavik, J., Kuntsi, J., Larsson, H., Lesch, K.-P., Ramos-Quiroga, J. A., Réthelyi, J. M., Ribases, M., & Reif, A. (2018). Live fast, die young? A review on the developmental trajectories of ADHD across the lifespan. European Neuropsychopharmacology: The Journal of the European College of Neuropsychopharmacology, 28(10), 1059–1088. https://doi.org/10.1016/j.euroneuro.2018.08.001 PubMed DOI
Gevensleben, H., Holl, B., Albrecht, B., Schlamp, D., Kratz, O., Studer, P., Wangler, S., Rothenberger, A., Moll, G. H., & Heinrich, H. (2009). Distinct EEG effects related to neurofeedback training in children with ADHD: A randomized controlled trial. International Journal of Psychophysiology, 74(2), 149–157. https://doi.org/10.1016/j.ijpsycho.2009.08.005 PubMed DOI
Gratton, G., Coles, M., & Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55(4), 468–484. https://doi.org/10.1016/0013-4694(83)90135-9 PubMed DOI
Guo, J., Luo, X., Li, B., Chang, Q., Sun, L., & Song, Y. (2020). Abnormal modulation of theta oscillations in children with attention-deficit/hyperactivity disorder. NeuroImage. Clinical, 27, 102314. https://doi.org/10.1016/j.nicl.2020.102314 PubMed DOI PMC
Heinrich, H., Busch, K., Studer, P., Erbe, K., Moll, G. H., & Kratz, O. (2014). EEG spectral analysis of attention in ADHD: Implications for neurofeedback training?. Frontiers in Human Neuroscience, 8, 611. https://doi.org/10.3389/fnhum.2014.00611 PubMed DOI PMC
Ishihara, T., Hayashi, H., & Hishikawa, Y. (1981). Distribution of frontal midline theta rhythm (Fm0) on the scalp in different states (mental calculation, resting and drowsiness). Electroencephalography and Clinical Neurophysiology, 52(3), 19. https://doi.org/10.1016/0013-4694(81)92408-1 DOI
Isler, J. R., Pini, N., Lucchini, M., Shuffrey, L. C., Morales, S., Bowers, M. E., Leach, S. C., Sania, A., Wang, L., Condon, C., Nugent, J. D., Elliott, A. J., Friedrich, C., Andrew, R., Fox, N. A., Myers, M. M., & Fifer, W. P. (2023). Longitudinal characterization of EEG power spectra during eyes open and eyes closed conditions in children. Psychophysiology, 60(1), e14158. https://doi.org/10.1111/psyp.14158 PubMed DOI
Janssen, T. W. P., Bink, M., Geladé, K., van Mourik, R., Maras, A., & Oosterlaan, J. (2016). A randomized controlled trial into the effects of neurofeedback, methylphenidate, and physical activity on EEG power spectra in children with ADHD. Journal of Child Psychology and Psychiatry, 57(5), 633–644. https://doi.org/10.1111/jcpp.12517 PubMed DOI
Kaiser, A., Aggensteiner, P.-M., Baumeister, S., Holz, N. E., Banaschewski, T., & Brandeis, D. (2020). Earlier versus later cognitive event-related potentials (ERPs) in attention-deficit/hyperactivity disorder (ADHD): A meta-analysis. Neuroscience and Biobehavioral Reviews, 112, 117–134. https://doi.org/10.1016/j.neubiorev.2020.01.019 PubMed DOI
Keute, M., Stenner, M.-P., Mueller, M.-K., Zaehle, T., & Krauel, K. (2019). Error-related dynamics of reaction time and frontal midline theta activity in attention deficit hyperactivity disorder (ADHD) during a subliminal motor priming task. Frontiers in Human Neuroscience, 13, 381. https://doi.org/10.3389/fnhum.2019.00381 PubMed DOI PMC
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2–3), 169–195. https://doi.org/10.1016/S0165-0173(98)00056-3 PubMed DOI
Krepel, N., Egtberts, T., Sack, A. T., Heinrich, H., Ryan, M., & Arns, M. (2020). A multicenter effectiveness trial of QEEG-informed neurofeedback in ADHD: Replication and treatment prediction. NeuroImage: Clinical, 28, 102399. https://doi.org/10.1016/j.nicl.2020.102399
Lithfous, S., Tromp, D., Dufour, A., Pebayle, T., Goutagny, R., & Després, O. (2015). Decreased theta power at encoding and cognitive mapping deficits in elderly individuals during a spatial memory task. Neurobiology of Aging, 36(10), 2821–2829. https://doi.org/10.1016/j.neurobiolaging.2015.07.007 PubMed DOI
Liu, Z.-X., Woltering, S., & Lewis, M. D. (2014). Developmental change in EEG theta activity in the medial prefrontal cortex during response control. NeuroImage, 85(Pt 2), 873–887. https://doi.org/10.1016/j.neuroimage.2013.08.054 PubMed DOI
Loo, S., & Arns, M. (2015). Should the EEG–based theta to beta ratio be used to diagnose ADHD? The ADHD Report, 23, 8–13. https://doi.org/10.1521/adhd.2015.23.8.8 DOI
Luu, P., Tucker, D. M., Derryberry, D., Reed, M., & Poulsen, C. (2003). Electrophysiological responses to errors and feedback in the process of action regulation. Psychological Science, 14(1), 47–53. https://doi.org/10.1111/1467-9280.01417 PubMed DOI
Maguire, M. J., & Schneider, J. M. (2019). Socioeconomic status related differences in resting state EEG activity correspond to differences in vocabulary and working memory in grade school. Brain and Cognition, 137, 103619. https://doi.org/10.1016/j.bandc.2019.103619 PubMed DOI
Marcos-Martínez, D., Santamaría-Vázquez, E., Martínez-Cagigal, V., Pérez-Velasco, S., Rodríguez-González, V., Martín-Fernández, A., Moreno-Calderón, S., & Hornero, R. (2023). ITACA: An open-source framework for neurofeedback based on brain-computer interfaces. Computers in Biology and Medicine, 160, 107011. https://doi.org/10.1016/j.compbiomed.2023.107011 PubMed DOI
Matsuura, M., Okubo, Y., Toru, M., Kojima, T., He, Y., Shen, Y., & Kyoon Lee, C. (1993). A cross-national EEG study of children with emotional and behavioral problems: A WHO collaborative study in the Western Pacific region. Biological Psychiatry, 34(1), 59–65. https://doi.org/10.1016/0006-3223(93)90257-E PubMed DOI
McLoughlin, G., Palmer, J. A., Rijsdijk, F., & Makeig, S. (2014). Genetic overlap between evoked frontocentral theta-band phase variability, reaction time variability, and attention-deficit/hyperactivity disorder symptoms in a twin study. Biological Psychiatry, 75(3), 238–247. https://doi.org/10.1016/j.biopsych.2013.07.020 PubMed DOI
McLoughlin, G., Gyurkovics, M., Palmer, J., & Makeig, S. (2022). Midfrontal theta activity in psychiatric illness: An index of cognitive vulnerabilities across disorders. Biological Psychiatry, 91(2), 173–182. https://doi.org/10.1016/j.biopsych.2021.08.020 PubMed DOI
Mitchell, D. J., McNaughton, N., Flanagan, D., & Kirk, I. J. (2008). Frontal-midline theta from the perspective of hippocampal „theta". Progress in Neurobiology, 86(3), 156–185. https://doi.org/10.1016/j.pneurobio.2008.09.005 PubMed DOI
Mohamed, S. M. H., Börger, N. A., Geuze, R. H., & van der Meere, J. J. (2019). Error monitoring and daily life executive functioning. Experimental Brain Research, 237(9), 2217–2229. https://doi.org/10.1007/s00221-019-05589-w PubMed DOI PMC
Neurofeedback Collaborative Group. (2023). Neurofeedback for attention-deficit/hyperactivity disorder: 25-month follow-up of double-blind randomized controlled trial. Journal of the American Academy of Child & Adolescent Psychiatry, 62(4), 435–446. https://doi.org/10.1016/j.jaac.2022.07.862 DOI
Orekhova, E. V., Stroganova, T. A., & Posikera, I. N. (1999). Theta synchronization during sustained anticipatory attention in infants over the second half of the first year of life. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 32(2), 151–172. https://doi.org/10.1016/s0167-8760(99)00011-2 PubMed DOI
Pascual-Marqui, R. D., Lehmann, D., Koenig, T., Kochi, K., Merlo, M. C., Hell, D., & Koukkou, M. (1999). Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Research, 90(3), 169–179. https://doi.org/10.1016/s0925-4927(99)00013-x PubMed DOI
Pérez-Elvira, R., Oltra-Cucarella, J., Carrobles, J. A., Moltó, J., Flórez, M., Parra, S., Agudo, M., Saez, C., Guarino, S., Costea, R. M., & Neamtu, B. (2021). Enhancing the effects of neurofeedback training: The motivational value of the reinforcers. Brain Sciences, 11(4), 457. https://doi.org/10.3390/brainsci11040457 PubMed DOI PMC
Perone, S., Palanisamy, J., & Carlson, S. M. (2018). Age-related change in brain rhythms from early to middle childhood: Links to executive function. Developmental Science, 21(6), e12691. https://doi.org/10.1111/desc.12691 PubMed DOI
Polanczyk, G. V., Salum, G. A., Sugaya, L. S., Caye, A., & Rohde, L. A. (2015). Annual research review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. Journal of Child Psychology and Psychiatry, 56(3), 345–365. https://doi.org/10.1111/jcpp.12381 PubMed DOI
Pscherer, C., Mueckschel, M., Summerer, L., Bluschke, A., & Beste, C. (2019). On the relevance of EEG resting theta activity for the neurophysiological dynamics underlying motor inhibitory control. Human Brain Mapping, 40(14), 4253–4265. https://doi.org/10.1002/hbm.24699 PubMed DOI PMC
Pscherer, C., Bluschke, A., Prochnow, A., Eggert, E., Mückschel, M., & Beste, C. (2020). Resting theta activity is associated with specific coding levels in event-related theta activity during conflict monitoring. Human Brain Mapping, 41(18), 5114–5127. https://doi.org/10.1002/hbm.25178 PubMed DOI PMC
Pscherer, C., Mückschel, M., Bluschke, A., & Beste, C. (2022). Resting-state theta activity is linked to information content-specific coding levels during response inhibition. Scientific Reports, 12, 4530. https://doi.org/10.1038/s41598-022-08510-8 PubMed DOI PMC
Sergeant, J. A., Geurts, H., Huijbregts, S., Scheres, A., & Oosterlaan, J. (2003). The top and the bottom of ADHD: A neuropsychological perspective. Neuroscience & Biobehavioral Reviews, 27(7), 583–592. https://doi.org/10.1016/j.neubiorev.2003.08.004 DOI
Shibata, K., Lisi, G., Cortese, A., Watanabe, T., Sasaki, Y., & Kawato, M. (2019). Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. NeuroImage, 188, 539–556. https://doi.org/10.1016/j.neuroimage.2018.12.022 PubMed DOI
Sibley, M. H., Swanson, J. M., Arnold, L. E., Hechtman, L. T., Owens, L. E., Stehli, A., Abikoff, H., Hinshaw, S. P., Molina, B. S. G., Mitchell, J. T., Jensen, P. S., Howard, A., Lakes, K. D., & Pelham, W. E. (2017). Defining ADHD symptom persistence in adulthood: Optimizing sensitivity and specificity. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 58(6), 655–662. https://doi.org/10.1111/jcpp.12620 PubMed DOI
Smit, D., Dapor, C., Koerts, J., Tucha, O. M., Huster, R. J., & Enriquez-Geppert, S. (2023). Long-term improvements in executive functions after frontal-midline theta neurofeedback in a (sub)clinical group. Frontiers in Human Neuroscience, 17, 1163380. https://doi.org/10.3389/fnhum.2023.1163380 PubMed DOI PMC
Snyder, H. R., Miyake, A., & Hankin, B. L. (2015). Advancing understanding of executive function impairments and psychopathology: Bridging the gap between clinical and cognitive approaches. Frontiers in Psychology, 6, 328. https://doi.org/10.3389/fpsyg.2015.00328 PubMed DOI PMC
Tan, E., Troller-Renfree, S. V., Morales, S., Buzzell, G. A., McSweeney, M., Antúnez, M., & Fox, N. A. (2024). Theta activity and cognitive functioning: Integrating evidence from resting-state and task-related developmental electroencephalography (EEG) research. Developmental Cognitive Neuroscience, 67, 101404. https://doi.org/10.1016/j.dcn.2024.101404 PubMed DOI PMC
Taschereau-Dumouchel, V., Cortese, A., Lau, H., & Kawato, M. (2021). Conducting decoded neurofeedback studies. Social Cognitive and Affective Neuroscience, 16(8), 838–848. https://doi.org/10.1093/scan/nsaa063 PubMed DOI
Vahid, A., Mückschel, M., Neuhaus, A., Stock, A.-K., & Beste, C. (2018). Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses. Scientific Reports, 8(1), 16235. https://doi.org/10.1038/s41598-018-34727-7 PubMed DOI PMC
van Dijk, H., van Wingen, G., Denys, D., Olbrich, S., van Ruth, R., & Arns, M. (2022). The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Scientific Data, 9(1), 333. https://doi.org/10.1038/s41597-022-01409-z PubMed DOI PMC
Van Meter, A. R., Sibley, M. H., Vandana, P., Birmaher, B., Fristad, M. A., Horwitz, S., Youngstrom, E. A., Findling, R. L., & Arnold, L. E. (2024). The stability and persistence of symptoms in childhood-onset ADHD. European Child & Adolescent Psychiatry, 33(4), 1163–1170. https://doi.org/10.1007/s00787-023-02235-3 DOI
Vanderwert, R. E., Zeanah, C. H., Fox, N. A., & Nelson, C. A. (2016). Normalization of EEG activity among previously institutionalized children placed into foster care: A 12-year follow-up of the Bucharest Early Intervention Project. Developmental Cognitive Neuroscience, 17, 68–75. https://doi.org/10.1016/j.dcn.2015.12.004 PubMed DOI
Vlahou, E. L., Thurm, F., Kolassa, I.-T., & Schlee, W. (2014). Resting-state slow wave power, healthy aging and cognitive performance. Scientific Reports, 4(1), 5101. https://doi.org/10.1038/srep05101 PubMed DOI PMC