Modulation of brain alpha rhythm and heart rate variability by attention-related mechanisms

Elisa Magosso; Giulia Ricci; Mauro Ursino; Elisa Magosso; Giulia Ricci; Mauro Ursino

doi:10.3934/Neuroscience.2019.1.1

AIMS Neuroscience

2019, Volume 6, Issue 1: 1-24. doi: 10.3934/Neuroscience.2019.1.1

Previous Article Next Article

Research article Recurring Topics

Modulation of brain alpha rhythm and heart rate variability by attention-related mechanisms

Department of Electrical, Electronic and Information Engineering, Campus of Cesena, University of Bologna, Cesena (FC), Italy

Received: 02 November 2018 Accepted: 12 February 2019 Published: 04 March 2019

According to recent evidence, oscillations in the alpha-band (8–14 Hz) play an active role in attention via allocation of cortical resources: decrease in alpha activity enhances neural processes in task-relevant regions, while increase in alpha activity reduces processing in task-irrelevant regions. Here, we analyzed changes in alpha-band power of 13-channel electroencephalogram (EEG) acquired from 30 subjects while performing four tasks that differently engaged visual, computational and motor attentional components. The complete (visual + computational + motor) task required to read and solve an arithmetical operation and provide a motor response; three simplified tasks involved a subset of these components (visual + computational task, visual task, motor task). Task-related changes in alpha power were quantified by aggregating electrodes into two main regions (fronto-central and parieto-occipital), to test regional specificity of alpha modulation depending on the involved attentional aspects. Independent Component Analysis (ICA) was applied to discover the main independent processes accounting for alpha power over the two scalp regions. Furthermore, we performed analysis of Heart Rate Variability (HRV) from one electrocardiogram signal acquired simultaneously with EEG, to test autonomic reaction to attentional loads. Results showed that alpha power modulation over the two scalp regions not only reflected the number of involved attentional components (the larger their number the larger the alpha power suppression) but was also fine-tuned by the nature of the recruited mechanisms (visual, computational, motor) relative to the functional specification of the regions. ICA revealed topologically dissimilar and differently attention-regulated processes of alpha power over the two regions. HRV indexes were less sensitive to different attentional aspects compared to alpha power, with vagal activity index presenting larger changes. This study contributes to improve our understanding of the electroencephalographic and autonomic correlates of attention and may have practical implications in neurofeedback, brain-computer interfaces, neuroergonomics as well as in clinical practice and neuroscience research exploring attention-deficit disorders.
- electroencephalography,
- alpha power,
- attention,
- Independent Component Analysis,
- Heart Rate Variability
Citation: Elisa Magosso, Giulia Ricci, Mauro Ursino. Modulation of brain alpha rhythm and heart rate variability by attention-related mechanisms[J]. AIMS Neuroscience, 2019, 6(1): 1-24. doi: 10.3934/Neuroscience.2019.1.1

Related Papers:

Abstract

According to recent evidence, oscillations in the alpha-band (8–14 Hz) play an active role in attention via allocation of cortical resources: decrease in alpha activity enhances neural processes in task-relevant regions, while increase in alpha activity reduces processing in task-irrelevant regions. Here, we analyzed changes in alpha-band power of 13-channel electroencephalogram (EEG) acquired from 30 subjects while performing four tasks that differently engaged visual, computational and motor attentional components. The complete (visual + computational + motor) task required to read and solve an arithmetical operation and provide a motor response; three simplified tasks involved a subset of these components (visual + computational task, visual task, motor task). Task-related changes in alpha power were quantified by aggregating electrodes into two main regions (fronto-central and parieto-occipital), to test regional specificity of alpha modulation depending on the involved attentional aspects. Independent Component Analysis (ICA) was applied to discover the main independent processes accounting for alpha power over the two scalp regions. Furthermore, we performed analysis of Heart Rate Variability (HRV) from one electrocardiogram signal acquired simultaneously with EEG, to test autonomic reaction to attentional loads. Results showed that alpha power modulation over the two scalp regions not only reflected the number of involved attentional components (the larger their number the larger the alpha power suppression) but was also fine-tuned by the nature of the recruited mechanisms (visual, computational, motor) relative to the functional specification of the regions. ICA revealed topologically dissimilar and differently attention-regulated processes of alpha power over the two regions. HRV indexes were less sensitive to different attentional aspects compared to alpha power, with vagal activity index presenting larger changes. This study contributes to improve our understanding of the electroencephalographic and autonomic correlates of attention and may have practical implications in neurofeedback, brain-computer interfaces, neuroergonomics as well as in clinical practice and neuroscience research exploring attention-deficit disorders.

Authors' contributions

E.M. contributed to design the study, to acquire the data, to analyze the data and interpret the results, drafted and edited the manuscript. G.R. acquired the data, contributed to analyze the data and interpret the results, and edited the manuscript. M.U. contributed to design the study, to analyze the data and interpret the results, and edited the manuscript.

Conflict of interest

The authors declare no conflict of interest.

References

[1]	Niedermeyer E, Lopes da Silva FH (1999) Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Philadelphia: Lippincott Williams and Wilkins.
[2]	Pfurtscheller G, Stancak Jr A, Neuper C (1996) Event-related synchronization (ERS) in the alpha band--an electrophysiological correlate of cortical idling: a review. Int J Psychophysiol 24: 39–46. doi: 10.1016/S0167-8760(96)00066-9
[3]	Jensen O, Mazaheri A (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci 4: 186.
[4]	Klimesch W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci 16: 606–617. doi: 10.1016/j.tics.2012.10.007
[5]	Foxe JJ, Snyder AC (2011) The role of Alpha-band brain oscillations as a sensory suppression mechanism during selective attention. Front Psychol 2: 154.
[6]	Frey JN, Ruhnau P, Weisz N (2015) Not so different after all: The same oscillatory processes support different types of attention. Brain Res 1626: 183–197. doi: 10.1016/j.brainres.2015.02.017
[7]	De Smedt B, Grabner RH, Studer B (2009) Oscillatory EEG correlates of arithmetic strategy use in addition and subtraction. Exp Brain Res 195: 635–642. doi: 10.1007/s00221-009-1839-9
[8]	Liang Y, Liu X, Qiu L, et al. (2018) An EEG study of a confusing state induced by information insufficiency during mathematical problem-solving and reasoning. Comput Intell Neurosci 2018: 1943565.
[9]	Yu X, Zhang J, Xie D, et al. (2009) Relationship between scalp potential and autonomic nervous activity during a mental arithmetic task. Auton Neurosci 146: 81–86. doi: 10.1016/j.autneu.2008.12.005
[10]	Benedek M, Schickel RJ, Jauk E, et al. (2014) Alpha power increases in right parietal cortex reflects focused internal attention. Neuropsychologia 56: 393–400. doi: 10.1016/j.neuropsychologia.2014.02.010
[11]	Jensen O, Gelfand J, Kounios J, et al. (2002) Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cereb Cortex 12: 877–882. doi: 10.1093/cercor/12.8.877
[12]	Tuladhar AM, ter Huurne N, Schoffelen JM, et al. (2007) Parieto-occipital sources account for the increase in alpha activity with working memory load. Hum Brain Mapp 28: 785–792. doi: 10.1002/hbm.20306
[13]	Chang YC, Huang SL (2012) The influence of attention levels on psychophysiological responses. Int J Psychophysiol 86: 39–47. doi: 10.1016/j.ijpsycho.2012.09.001
[14]	Grabner RH, Brunner C, Leeb R, et al. (2007) Event-related EEG theta and alpha band oscillatory responses during language translation. Brain Res Bull 72: 57–65. doi: 10.1016/j.brainresbull.2007.01.001
[15]	Grabner RH, De Smedt B (2011) Neurophysiological evidence for the validity of verbal strategy reports in mental arithmetic. Biol Psychol 87: 128–136. doi: 10.1016/j.biopsycho.2011.02.019
[16]	Fernandez T, Harmony T, Rodriguez M, et al. (1995) EEG activation patterns during the performance of tasks involving different components of mental calculation. Electroencephalogr Clin Neurophysiol 94: 175–182. doi: 10.1016/0013-4694(94)00262-J
[17]	Hairston WD, Whitaker KW, Ries AJ, et al. (2014) Usability of four commercially-oriented EEG systems. J Neural Eng 11: 046018. doi: 10.1088/1741-2560/11/4/046018
[18]	Grummett TS, Leibbrandt RE, Lewis TW, et al. (2015) Measurement of neural signals from inexpensive, wireless and dry EEG systems. Physiol Meas 36: 1469–1484. doi: 10.1088/0967-3334/36/7/1469
[19]	Mihajlovic V, Grundlehner B, Vullers R, et al. (2015) Wearable, wireless EEG solutions in daily life applications: what are we missing? IEEE J Biomed Health Inform 19: 6–21. doi: 10.1109/JBHI.2014.2328317
[20]	Luque-Casado A, Perales JC, Cardenas D, et al. (2016) Heart rate variability and cognitive processing: The autonomic response to task demands. Biol Psychol 113: 83–90. doi: 10.1016/j.biopsycho.2015.11.013
[21]	Krakauer J, Ghez C (2000) Voluntary movement, In: Kandel ER, Schwartz JH, Jessell TM, editors, Principles of Neural Science, 4th Edition ed., New York: McGraw-Hill, 756–781.
[22]	Babiloni C, Vecchio F, Miriello M, et al. (2006) Visuo-spatial consciousness and parieto-occipital areas: a high-resolution EEG study. Cereb Cortex 16: 37–46. doi: 10.1093/cercor/bhi082
[23]	Gobel SM, Calabria M, Farne A, et al. (2006) Parietal rTMS distorts the mental number line: simulating 'spatial' neglect in healthy subjects. Neuropsychologia 44: 860–868. doi: 10.1016/j.neuropsychologia.2005.09.007
[24]	Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7: 1129–1159. doi: 10.1162/neco.1995.7.6.1129
[25]	Lee TW, Girolami M, Sejnowski TJ (1999) Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput 11: 417–441. doi: 10.1162/089976699300016719
[26]	Makeig S, Onton J (2009) ERP features and EEG dynamics: An ICA perspective, In: Kappenman ES, Luck SJ, editors, The Oxford Handbook of Event-Related Potential Components, New York: Oxford University Press, 51–86.
[27]	Kaufmann T, Sutterlin S, Schulz SM, et al. (2011) ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis. Behav Res Methods 43: 1161–1170. doi: 10.3758/s13428-011-0107-7
[28]	Berntson GG, Quigley KS, Jang JF, et al. (1990) An approach to artifact identification: application to heart period data. Psychophysiology 27: 586–598. doi: 10.1111/j.1469-8986.1990.tb01982.x
[29]	Babiloni C, Carducci F, Cincotti F, et al. (1999) Human movement-related potentials vs desynchronization of EEG alpha rhythm: a high-resolution EEG study. Neuroimage 10: 658–665. doi: 10.1006/nimg.1999.0504
[30]	Manganotti P, Gerloff C, Toro C, et al. (1998) Task-related coherence and task-related spectral power changes during sequential finger movements. Electroencephalogr Clin Neurophysiol 109: 50–62. doi: 10.1016/S0924-980X(97)00074-X
[31]	Menon V, Rivera SM, White CD, et al. (2000) Dissociating prefrontal and parietal cortex activation during arithmetic processing. Neuroimage 12: 357–365. doi: 10.1006/nimg.2000.0613
[32]	Fournier LR, Wilson GF, Swain CR (1999) Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: manipulations of task difficulty and training. Int J Psychophysiol 31: 129–145. doi: 10.1016/S0167-8760(98)00049-X
[33]	Hansen AL, Johnsen BH, Thayer JF (2003) Vagal influence on working memory and attention. Int J Psychophysiol 48: 263–274. doi: 10.1016/S0167-8760(03)00073-4
[34]	Kubota Y, Sato W, Toichi M, et al. (2001) Frontal midline theta rhythm is correlated with cardiac autonomic activities during the performance of an attention demanding meditation procedure. Brain Res Cogn Brain Res 11: 281–287. doi: 10.1016/S0926-6410(00)00086-0
[35]	Duschek S, Worsching J, Reyes Del Paso GA (2015) Autonomic cardiovascular regulation and cortical tone. Clin Physiol Funct Imaging 35: 383–392. doi: 10.1111/cpf.12174
[36]	Triggiani AI, Valenzano A, Del Percio C, et al. (2016) Resting state Rolandic mu rhythms are related to activity of sympathetic component of autonomic nervous system in healthy humans. Int J Psychophysiol 103: 79–87. doi: 10.1016/j.ijpsycho.2015.02.009
[37]	Faes L, Nollo G, Jurysta F, et al. (2014) Information dynamics of brain–heart physiological networks during sleep. New J Phys 16: 105005. doi: 10.1088/1367-2630/16/10/105005

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)