Distinct neural mechanisms of alpha binaural beats and white noise for cognitive enhancement in young adults

Aini Ismafairus Abd Hamid; Nurfaten Hamzah; Siti Mariam Roslan; Nur Alia Amalin Suhardi; Muhammad Riddha Abdul Rahman; Faiz Mustafar; Hazim Omar; Asma Hayati Ahmad; Elza Azri Othman; Ahmad Nazlim Yusoff; Aini Ismafairus Abd Hamid; Nurfaten Hamzah; Siti Mariam Roslan; Nur Alia Amalin Suhardi; Muhammad Riddha Abdul Rahman; Faiz Mustafar; Hazim Omar; Asma Hayati Ahmad; Elza Azri Othman; Ahmad Nazlim Yusoff

doi:10.3934/Neuroscience.2025010

AIMS Neuroscience

2025, Volume 12, Issue 2: 147-179. doi: 10.3934/Neuroscience.2025010

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Research article Special Issues

Distinct neural mechanisms of alpha binaural beats and white noise for cognitive enhancement in young adults

1.
Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
2.
Brain & Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
3.
Hospital Pakar Universiti Sains Malaysia, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
4.
Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
5.
School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus, Terengganu, Malaysia
6.
Department of Physiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
7.
Center for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Science, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Wilayah Persekutuan Kuala Lumpur, Malaysia

Received: 06 February 2025 Revised: 29 April 2025 Accepted: 15 May 2025 Published: 20 May 2025

Young adulthood is a critical period marked by significant cognitive demands, requiring efficient brain function to manage academic, professional, and social challenges. Many young adults struggle with focus, stress management, and information processing. Emerging research suggests that auditory stimulation, specifically binaural beats and white noise, may enhance cognitive abilities and address these challenges. This exploratory study investigates the immediate effects of alpha binaural beats (ABB) and alpha binaural beats combined with white noise (AWN) on brain connectivity in young adults using functional magnetic resonance imaging (fMRI). Twenty-nine participants (n = 14 ABB, n = 15 AWN; mean age ≈ 22.14 years) were randomly assigned to receive either ABB or AWN during fMRI scans. Using dynamic independent component analysis (dyn-ICA), we examined the modulation of functional brain circuits during auditory stimulation. Preliminary findings revealed distinct and overlapping patterns of brain connectivity modulation of ABB and AWN. ABB primarily modulated connectivity within circuits involving frontoparietal, visual-motor, and multisensory regions, potentially enhancing cognitive flexibility, attentional control, and multisensory processing. Conversely, AWN primarily modulated connectivity in salience and default mode networks, with notable effects in limbic or reward regions, suggesting enhancements in focused attention and emotional processing. These preliminary results demonstrate that ABB and AWN differentially modulate brain networks on an immediate timescale. ABB may promote cognitive adaptability, while AWN enhances focused attention and emotional stability. Although behavioral effects were not assessed, these findings provide a neurobiological basis for understanding how these stimuli impact brain circuits. These preliminary findings may aid the development of personalized strategies for cognitive and emotional well-being. Given the exploratory nature, small sample size, and lack of concurrent behavioral data, these findings should be interpreted cautiously. Future research with rigorous designs, including control groups and behavioral measures, is needed to explore the long-term effects and applications of these interventions in various settings.
- alpha binaural beats,
- white noise,
- functional connectivity,
- fMRI,
- cognitive enhancements,
- young adults
Citation: Aini Ismafairus Abd Hamid, Nurfaten Hamzah, Siti Mariam Roslan, Nur Alia Amalin Suhardi, Muhammad Riddha Abdul Rahman, Faiz Mustafar, Hazim Omar, Asma Hayati Ahmad, Elza Azri Othman, Ahmad Nazlim Yusoff. Distinct neural mechanisms of alpha binaural beats and white noise for cognitive enhancement in young adults[J]. AIMS Neuroscience, 2025, 12(2): 147-179. doi: 10.3934/Neuroscience.2025010

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Abstract

Young adulthood is a critical period marked by significant cognitive demands, requiring efficient brain function to manage academic, professional, and social challenges. Many young adults struggle with focus, stress management, and information processing. Emerging research suggests that auditory stimulation, specifically binaural beats and white noise, may enhance cognitive abilities and address these challenges. This exploratory study investigates the immediate effects of alpha binaural beats (ABB) and alpha binaural beats combined with white noise (AWN) on brain connectivity in young adults using functional magnetic resonance imaging (fMRI). Twenty-nine participants (n = 14 ABB, n = 15 AWN; mean age ≈ 22.14 years) were randomly assigned to receive either ABB or AWN during fMRI scans. Using dynamic independent component analysis (dyn-ICA), we examined the modulation of functional brain circuits during auditory stimulation. Preliminary findings revealed distinct and overlapping patterns of brain connectivity modulation of ABB and AWN. ABB primarily modulated connectivity within circuits involving frontoparietal, visual-motor, and multisensory regions, potentially enhancing cognitive flexibility, attentional control, and multisensory processing. Conversely, AWN primarily modulated connectivity in salience and default mode networks, with notable effects in limbic or reward regions, suggesting enhancements in focused attention and emotional processing. These preliminary results demonstrate that ABB and AWN differentially modulate brain networks on an immediate timescale. ABB may promote cognitive adaptability, while AWN enhances focused attention and emotional stability. Although behavioral effects were not assessed, these findings provide a neurobiological basis for understanding how these stimuli impact brain circuits. These preliminary findings may aid the development of personalized strategies for cognitive and emotional well-being. Given the exploratory nature, small sample size, and lack of concurrent behavioral data, these findings should be interpreted cautiously. Future research with rigorous designs, including control groups and behavioral measures, is needed to explore the long-term effects and applications of these interventions in various settings.

Abbreviations

ABB

alpha binaural beats

EEG

electroencephalogram

white noise

AWN

alpha embedded binaural beats within WN

fMRI

functional magnetic resonance imaging

dyn-ICA

dynamic independent component analysis

USM

Universiti Sains Malaysia

WAIS-III

Wechsler Adult Intelligence Scale—Third Edition

EPI

echo-planar imaging

repetition time

echo time

acquisition time

SPM12

Statistical Parametric Mapping 12

MNI

Montreal Neurological Institute

BOLD

blood-oxygen-level-dependent

ROIs

regions of interest

FDR

false discovery rate

MVPA

multivoxel pattern analysis

Acknowledgments

This work was supported by Universiti Sains Malaysia, Short-Term Grant with Project No.: 304/PPSP/6315647. Additionally, we appreciate the support provided by the Bench Fee Program Sarjana Neurosains Kognitif 401/PPSP/E3170003. We extend our gratitude to the MRI technologists, Wan Nazyrah Abdul Halim, Che Munirah Che Abdullah, and Siti Afidah Mamat, for their invaluable assistance with data acquisition.

Funding

Universiti Sains Malaysia, Short-Term Grant with Project No.: 304/PPSP/6315647 and Bench Fee Program Sarjana Neurosains Kognitif 401/PPSP/E3170003.

Authors' contributions

All authors made direct and significant contributions to the manuscript. Aini Ismafairus Abd Hamid: Secured funding, conceptualized the study design, managed the project, wrote the original draft, and reviewed and edited the manuscript. Nurfaten Hamzah: Conducted data analysis and reviewed and edited the manuscript. Siti Mariam Roslan: Contributed to the study design, managed data collection, and conducted data analysis. Nur Alia Amalin Suhardi: Conducted data analysis and contributed to the literature review. Muhammad Riddha Abdul Rahman: Contributed to the literature review and reviewed the manuscript. Faiz Mustafar: Contributed to the study design and reviewed the manuscript. Hazim Omar: Contributed to the study design and reviewed the manuscript. Asma Hayati Ahmad, Elza Azri Othman, and Ahmad Nazlim Yusoff: Reviewed the manuscript. All authors approved the final version.

Conflict of interest

The authors declared no potential conflicts of interest.

References

[1]	Baddeley A (2003) Working memory: Looking back and looking forward. Nat Rev Neurosci 4: 829-839. https://doi.org/10.1038/nrn1201
[2]	Baddeley A, Hitch G, Allen R (2020) A Multicomponent Model of Working Memory. Working Memory: State of the Science : 10-43. https://doi.org/10.1093/oso/9780198842286.003.0002
[3]	Chai WJ, Abd Hamid AI, Abdullah JM (2018) Working memory from the psychological and neurosciences perspectives: A review. Front Psychol 9: 327922. https://doi.org/10.3389/fpsyg.2018.00401
[4]	Cowan N (2014) Working Memory Underpins Cognitive Development, Learning, and Education. Educ Psychol Rev 26: 197. https://doi.org/10.1007/s10648-013-9246-y
[5]	D'Esposito M, Postle BR (2015) The Cognitive Neuroscience of Working Memory. Annu Rev Psychol 66: 115. https://doi.org/10.1146/annurev-psych-010814-015031
[6]	Izmalkova A, Barmin A, Velichkovsky BB, et al. (2022) Cognitive Resources in Working Memory: Domain-Specific or General?. Behav Sci 12: 459. https://doi.org/10.3390/bs12110459
[7]	Davidow JY, Insel C, Somerville LH (2018) Adolescent Development of Value-Guided Goal Pursuit. Trends Cogn Sci 22: 725-736. https://doi.org/10.1016/j.tics.2018.05.003
[8]	Dwyer DB, Harrison BJ, Yücel M, et al. (2016) Adolescent Cognitive Control: Brain Network Dynamics. Stress: Concepts, Cognition, Emotion, and Behavior . Academic Press 177-185. https://doi.org/10.1016/B978-0-12-800951-2.00021-2
[9]	Larsen B, Luna B (2018) Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci Biobehav Rev 94: 179-195. https://doi.org/10.1016/j.neubiorev.2018.09.005
[10]	Marakushyn DI, Bulynina OD, Isaieva IM, et al. (2024) Impact of stress on emotional health and cognitive function. Med Sci Ukraine 20: 136-142. https://doi.org/10.32345/2664-4738.2.2024.16
[11]	Scullin MK, Bliwise DL (2015) Sleep, cognition, and normal aging: integrating a half century of multidisciplinary research. Perspect Psychol Sci 10: 97-137. https://doi.org/10.1177/1745691614556680
[12]	Zeek ML, Savoie MJ, Song M, et al. (2015) Sleep Duration and Academic Performance Among Student Pharmacists. Am J Pharm Educ 79: 63. https://doi.org/10.5688/ajpe79563
[13]	Rangesh HD, Ramanathan R, Mani V, et al. (2024) Study on stress perceived and the impact of stress on sleep among medical college students. Natl J Physiol Pharm Pharmacol 14: 348-352. https://doi.org/10.5688/ajpe79563
[14]	Pace-Schott EF, Spencer RMC (2011) Age-related changes in the cognitive function of sleep. Prog Brain Res 191: 75-89. https://doi.org/10.1016/B978-0-444-53752-2.00012-6
[15]	Belleville S, Cuesta M, Bier N, et al. (2024) Five-year effects of cognitive training in individuals with mild cognitive impairment. Alzheimers Dement (Amst) 16: e12626. https://doi.org/10.1002/dad2.12626
[16]	Mowszowski L, Batchelor J, Naismith SL (2010) Early intervention for cognitive decline: can cognitive training be used as a selective prevention technique?. Int Psychogeriatr 22: 537-548. https://doi.org/10.1017/S1041610209991748
[17]	Naismith SL, Glozier N, Burke D, et al. (2009) Early intervention for cognitive decline: is there a role for multiple medical or behavioural interventions?. Early Interv Psychiatry 3: 19-27. https://doi.org/10.1111/j.1751-7893.2008.00102.x
[18]	Leistiko NM, Madanat L, Yeung WKA, et al. (2023) Effects of gamma frequency binaural beats on attention and anxiety. Curr Psychol 1: 1. https://doi.org/10.1007/s12144-023-04681-3
[19]	Kim YJ, Kim KB, Kim JS, et al. (2023) Effects of inaudible binaural beats on visuospatial memory. Neuroreport 34: 501-505. https://doi.org/10.1097/WNR.0000000000001916
[20]	Aloysius NMF, Hamid AIA, Mustafar F (2023) Alpha and Low Gamma Embedded With White Noise Binaural Beats Modulating Working Memory among Malaysian Young Adult: A Preliminary fMRI Study. Malays J Medicine Health Sci 19: 113-124. https://doi.org/10.47836/mjmhs.19.1.17
[21]	Beauchene C, Abaid N, Moran R, et al. (2017) The effect of binaural beats on verbal working memory and cortical connectivity. J Neural Eng 14: 026014. https://doi.org/10.1088/1741-2552/aa5d67
[22]	Kim HW, Happe J, Lee YS (2023) Beta and gamma binaural beats enhance auditory sentence comprehension. Psychol Res 87: 2218-2227. https://doi.org/10.1007/s00426-023-01808-w
[23]	Baseanu ICC, Roman NA, Minzatanu D, et al. (2024) The Efficiency of Binaural Beats on Anxiety and Depression—A Systematic Review. Appl Sci 14: 5675. https://doi.org/10.3390/app14135675
[24]	Orozco Perez HD, Dumas G, Lehmann A (2020) Binaural Beats through the Auditory Pathway: From Brainstem to Connectivity Patterns. eNeuro 7: ENEURO.0232-19.2020. https://doi.org/10.1101/623231
[25]	Ingendoh RM, Posny ES, Heine A (2023) Binaural beats to entrain the brain? A systematic review of the effects of binaural beat stimulation on brain oscillatory activity, and the implications for psychological research and intervention. PLoS One 18: e0286023. https://doi.org/10.1371/journal.pone.0286023
[26]	Jurczyk J (2022) Influence of binaural beats on the state of relaxation and mood. Pharmac Psychiatry Neurol 37: 221-233. https://doi.org/10.5114/fpn.2021.115644
[27]	Rakhshan V, Hassani-Abharian P, Joghataei M, et al. (2022) Effects of the Alpha, Beta, and Gamma Binaural Beat Brain Stimulation and Short-Term Training on Simultaneously Assessed Visuospatial and Verbal Working Memories, Signal Detection Measures, Response Times, and Intrasubject Response Time Variabilities: A Within-Subject Randomized Placebo-Controlled Clinical Trial. Biomed Res Int 2022: 8588272. https://doi.org/10.1155/2022/8588272
[28]	Kraus J, Porubanová M (2015) The effect of binaural beats on working memory capacity. Stud Psychol (Bratisl) 57: 135-145. https://doi.org/10.21909/sp.2015.02.689
[29]	Klichowski M, Wicher A, Kruszwicka A, et al. (2023) Reverse effect of home-use binaural beats brain stimulation. Sci Rep 13: 11079. https://doi.org/10.1038/s41598-023-38313-4
[30]	López-Caballero F, Escera C (2017) Binaural beat: A failure to enhance EEG power and emotional arousal. Front Hum Neurosci 11: 244473. https://doi.org/10.3389/fnhum.2017.00557
[31]	Herweg NA, Bunzeck N (2015) Differential effects of white noise in cognitive and perceptual tasks. Front Psychol 6: 162351. https://doi.org/10.3389/fpsyg.2015.01639
[32]	Chen IC, Chan HY, Lin KC, et al. (2022) Listening to White Noise Improved Verbal Working Memory in Children with Attention-Deficit/Hyperactivity Disorder: A Pilot Study. Int J Environ Res Public Health 19: 7283. https://doi.org/10.3390/ijerph19127283
[33]	Helps SK, Bamford S, Sonuga-Barke EJS, et al. (2014) Different Effects of Adding White Noise on Cognitive Performance of Sub-, Normal and Super-Attentive School Children. PLoS One 9: e112768. https://doi.org/10.1371/journal.pone.0112768
[34]	Taitelbaum-Swead R, Fostick L (2016) The Effect of Age and Type of Noise on Speech Perception under Conditions of Changing Context and Noise Levels. Folia Phoniatr Logop 68: 16-21. https://doi.org/10.1159/000444749
[35]	Othman E, Yusoff AN, Mohamad M, et al. (2019) Low intensity white noise improves performance in auditory working memory task: An fMRI study. Heliyon 5: e02444. https://doi.org/10.1016/j.heliyon.2019.e02444
[36]	Yi JH, Kim KB, Kim YJ, et al. (2022) A Comparison of the Effects of Binaural Beats of Audible and Inaudible Frequencies on Brainwaves. Appl Sci 12: 13004. https://doi.org/10.3390/app122413004
[37]	Choi MH, Jung JJ, Kim KB, et al. (2022) Effect of binaural beat in the inaudible band on EEG (STROBE). Medicine 101: e29819. https://doi.org/10.1097/MD.0000000000029819
[38]	Whitehead AL, Julious SA, Cooper CL, et al. (2015) Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Stat Methods Med Res 25: 1057. https://doi.org/10.1177/0962280215588241
[39]	Ryan JJ, Lopez SJ (2001) Wechsler Adult Intelligence Scale-III. Understanding Psychological Assessment, Perspectives on Individual Differences . Boston, MA: Springer. https://doi.org/10.1007/978-1-4615-1185-4_2
[40]	Becker M, Repantis D, Dresler M, et al. (2022) Cognitive enhancement: Effects of methylphenidate, modafinil, and caffeine on latent memory and resting state functional connectivity in healthy adults. Hum Brain Mapp 43: 4225-4238. https://doi.org/10.1002/hbm.25949
[41]	Dutta A, Khan S, Akhtar Z (2023) Use of mobile phone based hearing app Hear WHO for self detection of hearing loss. J ENT Surg Res 1: 1-4.
[42]	World Health OrganizationhearWHO, 2019 (2019). Available from: https://www.who.int/teams/noncommunicable-diseases/sensory-functions-disability-and-rehabilitation/hearwho
[43]	Zaidil NN, Ying JH, Begum T, et al. (2019) Syntactic language processing among women - An EEG/ERP study of visual pictorial stimuli. 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) . Sarawak, Malaysia: 520-522. https://doi.org/10.1109/IECBES.2018.8626694
[44]	Nieto-Castanon A (2020) Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN . https://doi.org/10.56441/hilbertpress.2207.6598
[45]	Andersson JLR, Hutton C, Ashburner J, et al. (2001) Modeling geometric deformations in EPI time series. Neuroimage 13: 903-919. https://doi.org/10.1006/nimg.2001.0746
[46]	Friston KJ, Ashburner J, Frith CD, et al. (1995) Spatial registration and normalization of images. Hum Brain Mapp 3: 165-189. https://doi.org/10.1002/hbm.460030303
[47]	Henson R, Büchel C, Josephs O, et al. (1999) The Slice-Timing Problem in Event-related fMRI. Neuroimage 9: 125.
[48]	Sladky R, Friston KJ, Tröstl J, et al. (2011) Slice-timing effects and their correction in functional MRI. Neuroimage 58: 588-594. https://doi.org/10.1016/j.neuroimage.2011.06.078
[49]	Nieto-Castanon A (2022) Preparing fMRI Data for Statistical Analysis. arXiv : 2210.13564.
[50]	Power JD, Mitra A, Laumann TO, et al. (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84: 320-341. https://doi.org/10.1016/j.neuroimage.2013.08.048
[51]	Calhoun VD, Wager TD, Krishnan A, et al. (2017) The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. Hum Brain Mapp 38: 5331-5342. https://doi.org/10.1002/hbm.23737
[52]	Friston KJ, Williams S, Howard R, et al. (1996) Movement-related effects in fMRI time-series. Magn Reson Med 35: 346-355. https://doi.org/10.1002/mrm.1910350312
[53]	Hallquist MN, Hwang K, Luna B (2013) The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82: 208-225. https://doi.org/10.1016/j.neuroimage.2013.05.116
[54]	Desikan RS, Ségonne F, Fischl B, et al. (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31: 968-980. https://doi.org/10.1016/j.neuroimage.2006.01.021
[55]	Nieto-Castanon A, Whitfield-Gabrieli S CONN functional connectivity toolbox: RRID SCR_009550, release 22 (2022). https://doi.org/10.56441/hilbertpress.2246.5840
[56]	McLaren DG, Ries ML, Xu G, et al. (2012) A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches. Neuroimage 61: 1277-1286. https://doi.org/10.1016/j.neuroimage.2012.03.068
[57]	Friston KJ, Buechel C, Fink GR, et al. (1997) Psychophysiological and modulatory interactions in neuroimaging. Neuroimage 6: 218-229. https://doi.org/10.1006/nimg.1997.0291
[58]	Vinette SA, Bray S (2015) Variation in functional connectivity along anterior-to-posterior intraparietal sulcus, and relationship with age across late childhood and adolescence. Dev Cogn Neurosci 13: 32-42. https://doi.org/10.1016/j.dcn.2015.04.004
[59]	Osher DE, Brissenden JA, Somers DC (2019) Predicting an individual's dorsal attention network activity from functional connectivity fingerprints. J Neurophysiol 122: 232-240. https://doi.org/10.1152/jn.00174.2019
[60]	Van Ettinger-Veenstra HM, Huijbers W, Gutteling TP, et al. (2009) fMRI-guided TMS on cortical eye fields: the frontal but not intraparietal eye fields regulate the coupling between visuospatial attention and eye movements. J Neurophysiol 102: 3469-3480. https://doi.org/10.1152/jn.00350.2009
[61]	Petit L, Orssaud C, Tzourio N, et al. (1993) PET study of voluntary saccadic eye movements in humans: basal ganglia-thalamocortical system and cingulate cortex involvement. J Neurophysiol 69: 1009-1017. https://doi.org/101152/jn19936941009
[62]	Paré S, Bleau M, Dricot L, et al. (2023) Brain structural changes in blindness: a systematic review and an anatomical likelihood estimation (ALE) meta-analysis. Neurosci Biobehav Rev 150: 105165. https://doi.org/10.1016/j.neubiorev.2023.105165
[63]	Rilk AJ, Soekadar SR, Sauseng P, et al. (2011) Alpha coherence predicts accuracy during a visuomotor tracking task. Neuropsychologia 49: 3704-3709. https://doi.org/10.1016/j.neuropsychologia.2011.09.026
[64]	Lin CL, Shaw FZ, Young KY, et al. (2012) EEG correlates of haptic feedback in a visuomotor tracking task. Neuroimage 60: 2258-2273. https://doi.org/10.1016/j.neuroimage.2012.02.008
[65]	Park J, Kwon H, Kang S, et al. (2018) The effect of binaural beat-based audiovisual stimulation on brain waves and concentration. 2018 International Conference on Information and Communication Technology Convergence (ICTC) . IEEE 420-423. https://doi.org/10.1109/ICTC.2018.8539512
[66]	Shekar L, Suryavanshi CA, Nayak KR (2018) Effect of alpha and gamma binaural beats on reaction time and short-term memory. Natl J Physiol Pharm Pharmacol 8: 829-829. https://doi.org/10.5455/njppp.2018.8.1246506022018
[67]	Pehrs C, Zaki J, Schlochtermeier LH, et al. (2017) The Temporal Pole Top-Down Modulates the Ventral Visual Stream During Social Cognition. Cerebral Cortex 27: 777-792.
[68]	Hwang K, Shine JM, D'Esposito M (2017) Fronto-Parietal Interactions with Task-Evoked Functional Connectivity During Cognitive Control. BioRxi 2017: 133611. https://doi.org/10.1101/133611
[69]	Herlin B, Navarro V, Dupont S (2021) The temporal pole: From anatomy to function—A literature appraisal. J Chem Neuroanat 113: 101925. https://doi.org/10.1016/j.jchemneu.2021.101925
[70]	Nee DE (2021) Integrative frontal-parietal dynamics supporting cognitive control. Elife 10: e57244. https://doi.org/10.7554/eLife.57244
[71]	Braun U, Schäfer A, Walter H, et al. (2015) Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A 112: 11678-11683. https://doi.org/10.1073/pnas.1422487112
[72]	Burgos PI, Mariman JJ, Makeig S, et al. (2018) Visuomotor coordination and cortical connectivity of modular motor learning. Hum Brain Mapp 39: 3836-3853. https://doi.org/10.1002/hbm.24215
[73]	Ebrahiminia F, Hosseinzadeh G (2014) Modulation of effective connectivity during finger movement task with visual stimulus. 22nd Iranian Conference on Electrical Engineering . ICEE 1928-1932. https://doi.org/10.1109/IranianCEE.2014.6999857
[74]	Vine SJ, Moore LJ, Wilson MR (2016) An Integrative Framework of Stress, Attention, and Visuomotor Performance. Front Psychol 7. https://doi.org/10.3389/fpsyg.2016.01671
[75]	Sulik MJ, Haft SL, Obradović J (2018) Visual-Motor Integration, Executive Functions, and Academic Achievement: Concurrent and Longitudinal Relations in Late Elementary School. Early Educ Dev 29: 956-970. https://doi.org/10.1080/10409289.2018.1442097
[76]	Lin HY (2022) The Effects of White Noise on Attentional Performance and On-Task Behaviors in Preschoolers with ADHD. Int J Environ Res Public Health 19: 15391. https://doi.org/10.3390/ijerph192215391
[77]	Baijot S, Slama H, Söderlund G, et al. (2016) Neuropsychological and neurophysiological benefits from white noise in children with and without ADHD. Behav Brain Funct 12: 11. https://doi.org/10.1186/s12993-016-0095-y
[78]	Rausch VH, Bauch EM, Bunzeck N (2014) White Noise Improves Learning by Modulating Activity in Dopaminergic Midbrain Regions and Right Superior Temporal Sulcus. J Cogn Neurosci 26: 1469-1480. https://doi.org/10.1162/jocn_a_00537
[79]	Niemeier M, Goltz HC, Kuchinad A, et al. (2005) A Contralateral Preference in the Lateral Occipital Area: Sensory and Attentional Mechanisms. Cerebral Cortex 15: 325-331. https://doi.org/10.1093/cercor/bhh134
[80]	Keefe JM, Störmer VS (2021) Lateralized alpha activity and slow potential shifts over visual cortex track the time course of both endogenous and exogenous orienting of attention. Neuroimage 225: 117495. https://doi.org/10.1016/j.neuroimage.2020.117495
[81]	Guggenmos M, Thoma V, Haynes JD, et al. (2015) Spatial attention enhances object coding in local and distributed representations of the lateral occipital complex. Neuroimage 116: 149-157. https://doi.org/10.1016/j.neuroimage.2015.04.004
[82]	Wen X, Liu Y, Yao L, et al. (2013) Top-down regulation of default mode activity in spatial visual attention. J Neurosci 33: 6444-6453. https://doi.org/10.1523/JNEUROSCI.4939-12.2013
[83]	Nathan Spreng R, Schacter DL (2011) Default Network Modulation and Large-Scale Network Interactivity in Healthy Young and Old Adults. Cereb Cortex 22: 2610. https://doi.org/10.1093/cercor/bhr339
[84]	Nenert R, Allendorfer JB, Szaflarski JP (2014) A Model for Visual Memory Encoding. PLoS One 9: e107761. https://doi.org/10.1371/journal.pone.0107761
[85]	Kragel JE, Polyn SM (2015) Functional interactions between large-scale networks during memory search. Cereb Cortex 25: 667-679. https://doi.org/10.1093/cercor/bht258
[86]	DeViva JC, Zayfert C, Pigeon WR, et al. (2005) Treatment of residual insomnia after CBT for PTSD: Case studies. J Trauma Stress 18: 155-159. https://doi.org/10.1002/jts.20015
[87]	Wang R, Ge S, Zommara NM, et al. (2019) Auditory White Noise Affects Left/Right Visual Working Memory in an Opposite Pattern. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . Berlin, Germany: 2719-2722. https://doi.org/10.1109/EMBC.2019.8856688
[88]	Cole MW, Reynolds JR, Power JD, et al. (2013) Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci 16: 1348-1355. https://doi.org/10.1038/nn.3470
[89]	Cocuzza CV, Ito T, Schultz D, et al. (2020) Flexible coordinator and switcher hubs for adaptive task control. BioRxiv 822213. https://doi.org/10.1101/822213
[90]	Xia M, Xu P, Yang Y, et al. (2021) Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants. IEEE Access 9: 62316-62331. https://doi.org/10.1109/ACCESS.2021.3074220
[91]	Kwon S, Watanabe M, Fischer E, et al. (2017) Attention reorganizes connectivity across networks in a frequency specific manner. Neuroimage 144: 217-226. https://doi.org/10.1016/j.neuroimage.2016.10.014
[92]	Küchenhoff S, Sorg C, Schneider SC, et al. (2021) Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks. Eur J Neurosci 53: 3362-3377. https://doi.org/10.1111/ejn.15206
[93]	Chaieb L, Wilpert EC, Reber TP, et al. (2015) Auditory beat stimulation and its effects on cognition and mood states. Front Psychiatry 6: 136819. https://doi.org/10.3389/fpsyt.2015.00070
[94]	Garcia-Argibay M, Santed MA, Reales JM (2019) Efficacy of binaural auditory beats in cognition, anxiety, and pain perception: a meta-analysis. Psychol Res 83: 357-372. https://doi.org/10.1007/s00426-018-1066-8
[95]	Baars BJ, Geld N, Kozma R (2021) Global Workspace Theory (GWT) and Prefrontal Cortex: Recent Developments. Front Psychol 12. https://doi.org/10.3389/fpsyg.2021.749868
[96]	Dehaene S, Naccache L (2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79: 1-37. https://doi.org/10.1016/S0010-0277(00)00123-2
[97]	Friston K, FitzGerald T, Rigoli F, et al. (2017) Active Inference: A Process Theory. Neural Comput 29: 1-49. https://doi.org/10.1162/NECO_a_00912
[98]	Friston K (2010) The free-energy principle: a unified brain theory?. Nat Rev Neurosci 11: 127-138. https://doi.org/10.1038/nrn2787
[99]	Jiang LP, Rao RPN (2022) Predictive Coding Theories of Cortical Function. arXiv : 2112.10048. https://doi.org/10.1093/acrefore/9780190264086.013.328

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