nach oben

Erschienen in:

01.03.2025 | Original Paper

Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study

verfasst von: Zhidong Wang, Tie Sun, Feng Xiao

Erschienen in: Brain Topography | Ausgabe 2/2025

Einloggen, um Zugang zu erhalten

Abstract

Relational integration is a key subcomponent of working memory and a strong predictor of fluid intelligence. Both relational integration and fluid intelligence share a common neural foundation, particularly involving the frontoparietal network. This study utilized a randomized controlled experiment to examine the effect of relational integration training on brain networks using electroencephalogram (EEG) and microstate analysis. Participants were randomly assigned to either a relational integration training group (n = 29) or an active control group (n = 28) for one month. The Sandia matrices task assessed fluid intelligence, while rest-EEG was recorded during pre- and post-tests. Microstate analysis revealed that, for microstate D, the training group demonstrated a significant increase in occurrence and contribution following the intervention compared to the control group. Additionally, microstate D occurrence was negatively correlated with reaction times (RTs). Post-training, the training group showed a lower occurrence and contribution of microstate C compared to the control group. Regarding transfer probability, the training group exhibited a decrease between microstates A and B, and an increase between microstates C and D. In contrast, the control group showed increased transfer probability between microstates A, B, and C, and a decrease between microstate D and other microstates (B and A). These findings indicate that relational integration training influences frontoparietal networks associated with fluid intelligence. The current study suggests that relational integration training is an effective intervention for enhancing fluid intelligence.

Nur mit Berechtigung zugänglich

Andrews G, Halford GS, Chappell M, Maujean A, Shum DH (2014) Planning following stroke: a relational complexity approach using the tower of London. Front Hum Neurosci 8:1032. https://doi.org/10.3389/fnhum.2014.01032PubMedPubMedCentralCrossRef

Blacker KJ, Negoita S, Ewen JB, Courtney SM (2017) N-back versus complex span working memory training. J Cogn Enhancement 1:434–454. https://doi.org/10.1007/s41465-017-0044-1CrossRef

Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52(4):1162–1170. https://doi.org/10.1016/j.neuroimage.2010.02.052PubMedCrossRef

Chuderski A (2014) The relational integration task explains fluid reasoning above and beyond other working memory tasks. Mem Cognit 42(3):448–463. https://doi.org/10.3758/s13421-013-0366-xPubMedCrossRef

Chuderski A (2022) Fluid Intelligence emerges from representing relations. J Intell 10(3):51. https://doi.org/10.3390/jintelligence10030051PubMedPubMedCentralCrossRef

Clark CM, Lawlor-Savage L, Goghari VM (2017) Functional brain activation associated with working memory training and transfer. Behav Brain Res 334:34–49. https://doi.org/10.1016/j.bbr.2017.07.030PubMedCrossRef

Covey TJ, Shucard JL, Shucard DW (2019) Working memory training and perceptual discrimination training impact overlapping and distinct neurocognitive processes: evidence from event-related potentials and transfer of training gains. Cognition 182:50–72. https://doi.org/10.1016/j.cognition.2018.08.012PubMedCrossRef

Crone EA, Wendelken C, van Leijenhorst L, Honomichl RD, Christoff K, Bunge SA (2009) Neurocognitive development of relational reasoning. Dev Sci 12(1):55–66. https://doi.org/10.1111/j.1467-7687.2008.00743.xPubMedPubMedCentralCrossRef

Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009PubMedCrossRef

DeSerisy M, Ramphal B, Pagliaccio D, Raffanello E, Tau G, Marsh R, Margolis AE (2021) Frontoparietal and default mode network connectivity varies with age and intelligence. Dev Cogn Neurosci 48:8., Article 100928. https://doi.org/10.1016/j.dcn.2021.100928CrossRef

Douw L, Wakeman DG, Tanaka N, Liu H, Stufflebeam SM (2016) State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience 339:12–21. https://doi.org/10.1016/j.neuroscience.2016.09.034PubMedCrossRef

Duncan J (2010) The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn Sci 14(4):172–179. https://doi.org/10.1016/j.tics.2010.01.004PubMedCrossRef

Halford GS, Wilson WH, Phillips S (1998) Processing capacity defined by relational complexity: implications for comparative, developmental, and cognitive psychology. Behav Brain Sci 21(6):803–831. https://doi.org/10.1017/S0140525X98001769PubMedCrossRef

Hannon B, Daneman M (2014) Revisiting the construct of relational integration and its role in accounting for general intelligence: the importance of knowledge integration. Intelligence 47:175–187. https://doi.org/10.1016/j.intell.2014.09.010CrossRef

Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A (2018) Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI. Comput Biol Med 102:30–39. https://doi.org/10.1016/j.compbiomed.2018.09.004PubMedCrossRef

Holmes J, Woolgar F, Hampshire A, Gathercole SE (2019) Are Working Memory Training effects Paradigm-Specific? Frontiers in psychology. 10., Article 1103. https://doi.org/10.3389/fpsyg.2019.01103

Holyoak KJ, Monti MM (2021) Relational integration in the human brain: a review and synthesis. J Cogn Neurosci 33(3):341–356. https://doi.org/10.1162/jocn_a_01619PubMedCrossRef

Holzman TG, Pellegrino JW, Glaser R (1983) Cognitive variables in series completion. J Educ Psychol 75(4):603. https://doi.org/10.1037/0022-0663.75.4.603CrossRef

Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ (2008) Improving fluid intelligence with training on working memory. PNAS. https://doi.org/10.1073/pnas.080126810

Jastrzębski J, Ociepka M, Chuderski A (2020) Fluid reasoning is equivalent to relation processing. Intell 82. https://doi.org/10.1016/j.intell.2020.101489

Jung RE, Haier RJ (2007) The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav Brain Sci 30(2):135–154. https://doi.org/10.1017/S0140525X07001185PubMedCrossRef

Khanna A, Pascual-Leone A, Farzan F (2014) Reliability of resting-state microstate features in electroencephalography. PLoS ONE 9(12):e114163. https://doi.org/10.1371/journal.pone.0114163PubMedPubMedCentralCrossRef

Khanna A, Pascual-Leone A, Michel CM, Farzan F (2015) Microstates in resting-state EEG: current status and future directions. Neurosci Biobehavioral Reviews 49:105–113. https://doi.org/10.1016/j.neubiorev.2014.12.010CrossRef

Khazaee A, Ebrahimzadeh A, Babajani-Feremi A (2017) Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI. Behav Brain Res 322:339–350. https://doi.org/10.1016/j.bbr.2016.06.043PubMedCrossRef

Kleinert T, Koenig T, Nash K, Wascher E (2024) On the reliability of the EEG microstate approach. Brain Topogr 37(2):271–286. https://doi.org/10.1007/s10548-023-00982-9PubMedCrossRef

Koenig T, Prichep L, Lehmann D, Sosa PV, Braeker E, Kleinlogel H, John ER (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. NeuroImage 16(1):41–48. https://doi.org/10.1006/nimg.2002.1070PubMedCrossRef

Koenig T, Kottlow M, Stein M, Melie-García L (2011) Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics. Comput Intell Neurosci, 2011, 938925. https://doi.org/10.1155/2011/938925

Langer N, von Bastian CC, Wirz H, Oberauer K, Jancke L (2013) The effects of working memory training on functional brain network efficiency. Cortex 49(9):2424–2438. https://doi.org/10.1016/j.cortex.2013.01.008PubMedCrossRef

Lawlor-Savage L, Clark CM, Goghari VM (2019) No evidence that working memory training alters gray matter structure: a MRI surface-based analysis. Behav Brain Res 360:323–340. https://doi.org/10.1016/j.bbr.2018.12.008PubMedCrossRef

Lawlor-Savage L, Kusi M, Clark CM, Goghari VM (2021) No evidence for an effect of a working memory training program on white matter microstructure. Intelligence 86:101541. https://doi.org/10.1016/j.intell.2021.101541CrossRef

Lehmann D, Faber PL, Galderisi S, Herrmann WM, Kinoshita T, Koukkou M, Wackermann J (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Research: Neuroimaging 138(2):141–156. https://doi.org/10.1016/j.pscychresns.2004.05.007CrossRef

Liu JY, Xu J, Zou GY, He Y, Zou QH, Gao JH (2020) Reliability and individual specificity of EEG Microstate Characteristics. Brain Topogr 33(4):438–449. https://doi.org/10.1007/s10548-020-00777-2PubMedCrossRef

Matysiak O, Kroemeke A, Brzezicka A (2019) Working Memory Capacity as a predictor of cognitive training efficacy in the Elderly Population. Front Aging Neurosci 11:126. https://doi.org/10.3389/fnagi.2019.00126PubMedPubMedCentralCrossRef

Matzen LE, Benz ZO, Dixon KR, Posey J, Kroger JK, Speed AE (2010) Recreating Raven’s: Software for systematically generating large numbers of raven-like matrix problems with normed properties. Behav Res Methods 42(2):525–541. https://doi.org/10.3758/BRM.42.2.525PubMedCrossRef

Michel CM, Koenig T (2018) EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. NeuroImage 180:577–593. https://doi.org/10.1016/j.neuroimage.2017.11.062PubMedCrossRef

Miller Singley AT, Bunge SA (2014) Neurodevelopment of relational reasoning: implications for mathematical pedagogy. Trends Neurosci Educ 3(2):33–37. https://doi.org/10.1016/j.tine.2014.03.001CrossRef

Miro-Padilla A, Bueicheku E, Avila C (2020) Locating neural transfer effects of n-back training on the central executive: a longitudinal fMRI study. Sci Rep 10(1). https://doi.org/10.1038/s41598-020-62067-y

Miró-Padilla A, Bueichekú E, Ventura-Campos N, Flores-Compañ M-J, Parcet MA, Ávila C (2019) Long-term brain effects of N-back training: an fMRI study. Brain Imaging Behav 13(4):1115–1127. https://doi.org/10.1007/s11682-018-9925-xPubMedCrossRef

Momi D, Neri F, Coiro G, Smeralda C, Veniero D, Sprugnoli G, Santarnecchi E (2020) Cognitive enhancement via Network-targeted cortico-cortical associative brain stimulation. Cereb Cortex 30(3):1516–1527. https://doi.org/10.1093/cercor/bhz182PubMedCrossRef

Musaeus CS, Nielsen MS, Høgh P (2019) Microstates as disease and progression markers in patients with mild cognitive impairment. Front NeuroSci 13. https://doi.org/10.3389/fnins.2019.00563

Musaeus CS, Engedal K, Høgh P, Jelic V, Khanna AR, Kjær TW, Andersen BB (2020) Changes in the left temporal microstate are a sign of cognitive decline in patients with Alzheimer’s disease. Brain Behav 10(6):e01630. https://doi.org/10.1002/brb3.1630PubMedPubMedCentralCrossRef

Necka E, Gruszka A, Hampshire A, Sarzynska-Wawer J, Anicai AE, Orzechowski J, Soreq E (2021) The effects of Working Memory Training on Brain Activity. Brain Sci 11(2):21 Article 155. https://doi.org/10.3390/brainsci11020155CrossRef

Nishida K, Morishima Y, Yoshimura M, Isotani T, Irisawa S, Jann K, Koenig T (2013) EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and Alzheimer’s disease. Clin Neurophysiol 124(6):1106–1114. https://doi.org/10.1016/j.clinph.2013.01.005PubMedCrossRef

Oberauer K, Süß H-M, Wilhelm O, Wittman WW (2003) The multiple faces of working memory: Storage, processing, supervision, and coordination. Intelligence 31(2):167–193. https://doi.org/10.1016/S0160-2896(02)00115-0CrossRef

Oberauer K, Süβ H-M, Wilhelm O, Wittmann WW (2008) Which working memory functions predict intelligence? Intelligence 36(6):641–652. https://doi.org/10.1016/j.intell.2008.01.007CrossRef

Oberauer K, Lewandowsky S, Awh E, Brown GD, Conway A, Cowan N, Hurlstone MJ (2018) Benchmarks for models of short-term and working memory. Psychol Bull 144(9):885. https://doi.org/10.1037/bul0000153PubMedCrossRef

Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng 42(7):658–665. https://doi.org/10.1109/10.391164PubMedCrossRef

Rajkumar R, Farrher E, Mauler J, Sripad P, Régio Brambilla C, Kops R, Neuner E, I (2021) Comparison of EEG microstates with resting state fMRI and FDG-PET measures in the default mode network via simultaneously recorded trimodal (PET/MR/EEG) data. Hum Brain Mapp 42(13):4122–4133. https://doi.org/10.1002/hbm.24429PubMedCrossRef

Sala G, Aksayli ND, Tatlidil KS, Gondo Y, Gobet F (2019) Working memory training does not enhance older adults’ cognitive skills: a comprehensive meta-analysis. Intelligence 77:101386. https://doi.org/10.1016/j.intell.2019.101386CrossRef

Salminen T, Martensson J, Schubert T, Kuhn S (2016) Increased integrity of white matter pathways after dual n-back training. NeuroImage 133:244–250. https://doi.org/10.1016/j.neuroimage.2016.03.028PubMedCrossRef

Santarnecchi E, Khanna AR, Musaeus CS, Benwell CSY, Davila P, Farzan F, Honeywell ST (2017) a. EEG Microstate Correlates of Fluid Intelligence and Response to Cognitive Training. Brain topography, 30(4), 502–520. https://doi.org/10.1007/s10548-017-0565-z

Smailovic U, Koenig T, Laukka EJ, Kalpouzos G, Andersson T, Winblad B, Jelic V (2019) EEG time signature in Alzheimer s disease: functional brain networks falling apart. NeuroImage: Clin 24:102046. https://doi.org/10.1016/j.nicl.2019.102046PubMedCrossRef

Tait L, Tamagnini F, Stothart G, Barvas E, Monaldini C, Frusciante R, Goodfellow M (2020) EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease. Sci Rep 10(1):17627. https://doi.org/10.1038/s41598-020-74790-7PubMedPubMedCentralCrossRef

Tarailis P, Koenig T, Michel CM, Griškova-Bulanova I (2023) The functional aspects of resting EEG microstates: A Systematic Review. Brain topography, 1–37. https://doi.org/10.1007/s10548-023-00958-9

Van De Ville D, Britz J, Michel CM (2010) EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proceedings of the National Academy of Sciences, 107(42), 18179–18184. https://doi.org/10.1073/pnas.1007841107

Viskontas IV, Morrison RG, Holyoak KJ, Hummel JE, Knowlton BJ (2004) Relational integration, inhibition, and analogical reasoning in older adults. Psychol Aging 19(4):581. https://doi.org/10.1037/0882-7974.19.4.581PubMedCrossRef

Wendelken C, Hare ED, Whitaker KJ, Ferrer E, Bunge SA (2011) Increased functional selectivity over development in Rostrolateral Prefrontal Cortex. J Neurosci 31(47):17260. https://doi.org/10.1523/JNEUROSCI.1193-10.2011PubMedPubMedCentralCrossRef

Wendelken C, Chung D, Bunge SA (2012) Rostrolateral prefrontal cortex: domain-general or domain-sensitive? Hum Brain Mapp 33(8):1952–1963. https://doi.org/10.1002/hbm.21336PubMedCrossRef

Wendelken C, Ferrer E, Whitaker KJ, Bunge SA (2016) Fronto-Parietal Network Reconfiguration supports the development of reasoning ability. Cereb Cortex 26(5):2178–2190. https://doi.org/10.1093/cercor/bhv050PubMedCrossRef

Wright SB, Matlen BJ, Baym CL, Ferrer E, Bunge SA (2007) Neural correlates of fluid reasoning in children and adults. Front Hum Neurosci 1:8. https://doi.org/10.3389/neuro.09.008.2007PubMed

Xiao F, Li P, Long CQ, Lei Y, Li H (2014) Relational complexity modulates activity in the prefrontal cortex during numerical inductive reasoning: an fMRI study. Biol Psychol 101:61–68. https://doi.org/10.1016/j.biopsycho.2014.06.005CrossRef

Xiao F, Wang ZD, Yuan SQ, Liang K, Chen Q (2022) Relational integration predicted numerical inductive reasoning: ERP evidence from the N400 and LNC. Psychophysiology e14046. https://doi.org/10.1111/psyp.14046

Zanesco AP (2024) Normative temporal dynamics of resting EEG microstates. Brain Topogr 37(2):243–264. https://doi.org/10.1007/s10548-023-01004-4PubMedCrossRef

Zappasodi F, Perrucci MG, Saggino A, Croce P, Mercuri P, Romanelli R, Ebisch SJH (2019) EEG microstates distinguish between cognitive components of fluid reasoning. NeuroImage 189:560–573. https://doi.org/10.1016/j.neuroimage.2019.01.067PubMedCrossRef

Titel: Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study
verfasst von: Zhidong Wang
Tie Sun
Feng Xiao
Publikationsdatum: 01.03.2025
Verlag: Springer US
Erschienen in: Brain Topography / Ausgabe 2/2025
Print ISSN: 0896-0267
Elektronische ISSN: 1573-6792
DOI: https://doi.org/10.1007/s10548-024-01099-3

EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression

Original Paper

Abnormal Alterations of the White Matter Structural Network in Patients with Herpes Zoster and Postherpetic Neuralgia

Research

Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke

Original Paper

Brain Dynamics of Speech Modes Encoding: Loud and Whispered Speech Versus Standard Speech

Open Access
Original Paper

Network Abnormalities in Ischemic Stroke: A Meta-analysis of Resting-State Functional Connectivity

Review

Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach

Original Paper

Kompaktes Leitlinien-Wissen Neurologie (Link öffnet in neuem Fenster)

Mit medbee Pocketcards schnell und sicher entscheiden.
Leitlinien-Wissen kostenlos und immer griffbereit auf ihrem Desktop, Handy oder Tablet.

Kostenlos registrieren

Neu im Fachgebiet Neurologie

Neuartige Antikörpertherapie bremst MS über zwei Jahre hinweg

18.04.2025
AAN-Jahrestagung 2025
Nachrichten

Eine Therapie mit dem C40-Ligand-Blocker Frexalimab kann MS-Schübe und neue MRT-Läsionen über zwei Jahre hinweg verhindern. Dafür spricht die Auswertung einer offen fortgeführten Phase-2-Studie.

Therapiestopp bei älteren MS-Kranken kann sich lohnen

17.04.2025
AAN-Jahrestagung 2025
Nachrichten

Eine Analyse aus Kanada bestätigt: Setzen ältere MS-Kranke die Behandlung mit Basistherapeutika ab, müssen sie kaum mit neuen Schüben und MRT-Auffälligkeiten rechnen.

Positive Phase IIb-Studie zu spezifischer CAR-T-Zell-Therapie bei Myasthenia gravis

17.04.2025
AAN-Jahrestagung 2025
Kongressbericht

Eine auf das B-Zell-Reifungsantigen gerichtete mRNA-basierte CAR-T-Zell-Therapie wurde jetzt in einer ersten Phase IIb-Studie zur Behandlung der generalisierten Myasthenia gravis mit Placebo verglichen.

Schadet Schichtarbeit dem Gehirn?

17.04.2025
AAN-Jahrestagung 2025
Kongressbericht

Eine große Registerstudie bestätigt, dass Schichtarbeit mit einem erhöhten Risiko für psychische und neurologische Erkrankungen einhergeht, sowie mit einer Volumenabnahme in Gehirnarealen, die für Depression, Angst und kognitive Funktionen relevant sind.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study

Abstract

Weitere Artikel der Ausgabe 2/2025

EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression

Abnormal Alterations of the White Matter Structural Network in Patients with Herpes Zoster and Postherpetic Neuralgia

Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke

Brain Dynamics of Speech Modes Encoding: Loud and Whispered Speech Versus Standard Speech

Network Abnormalities in Ischemic Stroke: A Meta-analysis of Resting-State Functional Connectivity

Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach

Kompaktes Leitlinien-Wissen Neurologie (Link öffnet in neuem Fenster)

Neu im Fachgebiet Neurologie

Neuartige Antikörpertherapie bremst MS über zwei Jahre hinweg

Therapiestopp bei älteren MS-Kranken kann sich lohnen

Positive Phase IIb-Studie zu spezifischer CAR-T-Zell-Therapie bei Myasthenia gravis

Schadet Schichtarbeit dem Gehirn?

Update Neurologie

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten