Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Abstract

Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuroelectrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes, such as in center frequency or bandwidth, and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features and find that multiple disparate features influence ratio measures. For example, the commonly applied θ/β ratio is most reflective of differences in aperiodic activity, and not oscillatory θ or β power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 61

1. 1.

   Angelidis A, van der Does W, Schakel L, Putman P (2016 ) Frontal EEG theta/beta ratio as an electrophysiological marker for attentional control and its test-retest reliability. Biol Psychol 121:49–52.

   Not in our library10.1016/j.biopsycho.2016.09.008

2. 2.

   Arns M, Conners CK, Kraemer HC (2013 ) A decade of EEG theta/beta ratio research in ADHD: a meta-analysis. J Atten Disord 17:374–383.

   Not in our library10.1177/1087054712460087

3. 3.

   Barry RJ, Clarke AR, Johnstone SJ (2003 ) A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol 114:171–183.

   Not in our library10.1016/s1388-2457(02)00362-0

4. 4.

   Bazanova OM, Auer T, Sapina EA (2018 ) On the efficiency of individualized theta/beta ratio neurofeedback combined with forehead EMG training in ADHD children. Front Hum Neurosci 12:3.

   Not in our library10.3389/fnhum.2018.00003

5. 5.

   Bennys K, Rondouin G, Vergnes C, Touchon J (2001 ) Diagnostic value of quantitative EEG in Alzheimer’s disease. Neurophysiol Clin 31:153–160.

   Not in our library10.1016/s0987-7053(01)00254-4

6. 6.

   Bresnahan SM, Anderson JW, Barry RJ (1999 ) Age-related changes in quantitative EEG in attention-deficit/hyperactivity disorder. Biol Psychiatry 46:1690–1697.

   Not in our library10.1016/s0006-3223(99)00042-6

7. 7.

   Buyck I, Wiersema JR (2014 ) State-related electroencephalographic deviances in attention deficit hyperactivity disorder. Res Dev Disabil 35:3217–3225.

   Not in our library10.1016/j.ridd.2014.08.003

8. 8.

   Buzsáki G, Logothetis N, Singer W (2013 ) Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron 80:751–764.

   Not in our library10.1016/j.neuron.2013.10.002

9. 9.
   Systematic Review on Resting-State EEG for Alzheimer’s Disease Diagnosis and Progression Assessment · 2018

   No notice
10. 10.

    Clarke AR, Barry RJ, McCarthy R, Selikowitz M (2001 ) Age and sex effects in the EEG: development of the normal child. Clin Neurophysiol 112:806–814.

    Not in our library10.1016/s1388-2457(01)00488-6

11. 11.

    Clarke AR, Barry RJ, Dupuy FE, McCarthy R, Selikowitz M, Johnstone SJ (2013 ) Excess beta activity in the EEG of children with attention-deficit/hyperactivity disorder: a disorder of arousal? Int J Psychophysiol 89:314–319.

    Not in our library10.1016/j.ijpsycho.2013.04.009

12. 12.

    Costa-Miserachs D, Portell-Cortés I, Torras-Garcia M, Morgado-Bernal I (2003 ) Automated sleep staging in rat with a standard spreadsheet. J Neurosci Methods 130:93–101.

    Not in our library10.1016/s0165-0270(03)00229-2

13. 13.

    Donoghue T (2019 ) LISC: a Python package for scientific literature collection and analysis. JOSS 4:1674

    Not in our library10.21105/joss.01674

14. 14.

    Donoghue T, Haller M, Peterson E, Varma P, Sebastian P, Gao R, Noto T, Lara AH, Wallis JD, Knight RT, Shestyuk A, Voytek B (2020 ) Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci, in press.

    Not in our library10.1038/s41593-020-00744-x

15. 15.

    Egner T, Zech TF, Gruzelier JH (2004 ) The effects of neurofeedback training on the spectral topography of the electroencephalogram. Clin Neurophysiol 115:2452–2460.

    Not in our library10.1016/j.clinph.2004.05.033

16. 16.

    Gao R, Peterson EJ, Voytek B (2017 ) Inferring synaptic excitation/inhibition balance from field potentials. Neuroimage 158:70–78.

    Not in our library10.1016/j.neuroimage.2017.06.078

17. 17.

    Gasser T, Verleger R, Bächer P, Sroka L (1988 ) Development of the EEG of school-age children and adolescents. I. Analysis of band power. Electroencephalogr Clin Neurophysiol 69:91–99.

    Not in our library10.1016/0013-4694(88)90204-0

18. 18.

    Geraedts VJ, Marinus J, Gouw AA, Mosch A, Stam CJ, van Hilten JJ, Contarino MF, Tannemaat MR (2018 ) Quantitative EEG reflects non-dopaminergic disease severity in Parkinson’s disease. Clin Neurophysiol 129:1748–1755.

    Not in our library10.1016/j.clinph.2018.04.752

19. 19.

    Gloss D, Varma JK, Pringsheim T, Nuwer MR (2016 ) Practice advisory: the utility of EEG theta/beta power ratio in ADHD diagnosis: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology 87:2375–2379.

    Not in our library10.1212/wnl.0000000000003265

20. 20.

    Gordon S, Todder D, Deutsch I, Garbi D, Getter N, Meiran N (2018 ) Are resting state spectral power measures related to executive functions in healthy young adults? Neuropsychologia 108:61–72.

    Not in our library10.1016/j.neuropsychologia.2017.10.031

21. 21.

    Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Parkkonen L, Hämäläinen MS (2014 ) MNE software for processing MEG and EEG data. Neuroimage 86:446–460.

    Not in our library10.1016/j.neuroimage.2013.10.027

22. 22.

    He BJ (2014 ) Scale-free brain activity: past, present, and future. Trends Cogn Sci 18:480–487.

    Not in our library10.1016/j.tics.2014.04.003

23. 23.

    He W, Donoghue T, Sowman PF, Seymour RA, Brock J, Crain S, Voytek B, Hillebrand A (2019 ) Co-increasing neuronal noise and beta power in the developing brain. BioRxiv. doi: https://doi.org/10.1101/839258.

    Not in our library

24. 24.

    Howells FM, Temmingh HS, Hsieh JH, van Dijen AV, Baldwin DS, Stein DJ (2018 ) Electroencephalographic delta/alpha frequency activity differentiates psychotic disorders: a study of schizophrenia, bipolar disorder and methamphetamine-induced psychotic disorder. Transl Psychiatry 8:75.

    Not in our library10.1038/s41398-018-0105-y

25. 25.

    Jurewicz K, Paluch K, Kublik E, Rogala J, Mikicin M, Wróbel A (2018 ) EEG-neurofeedback training of beta band (12–22 Hz) affects alpha and beta frequencies – a controlled study of a healthy population. Neuropsychologia 108:13–24.

    Not in our library10.1016/j.neuropsychologia.2017.11.021

26. 26.

    Keune PM, Hansen S, Weber E, Zapf F, Habich J, Muenssinger J, Wolf S, Schönenberg M, Oschmann P (2017 ) Exploring resting-state EEG brain oscillatory activity in relation to cognitive functioning in multiple sclerosis. Clin Neurophysiol 128:1746–1754.

    Not in our library10.1016/j.clinph.2017.06.253

27. 27.
    Developmental Changes in Hippocampal CA1 Single Neuron Firing and Theta Activity during Associative Learning. · 2016

    Not checked
28. 28.

    Klimesch W (1999 ) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev 29:169–195.

    Not in our library10.1016/s0165-0173(98)00056-3

29. 29.

    Krakovská A, Mezeiová K (2011 ) Automatic sleep scoring: a search for an optimal combination of measures. Artif Intell Med 53:25–33.

    Not in our library10.1016/j.artmed.2011.06.004

30. 30.

    Langer N, Ho EJ, Alexander LM, Xu HY, Jozanovic RK, Henin S, Petroni A, Cohen S, Marcelle ET, Parra LC, Milham MP, Kelly SP (2017 ) A resource for assessing information processing in the developing brain using EEG and eye tracking. Sci Data 4:170040

    Not in our library10.1038/sdata.2017.40

31. 31.

    Lansbergen MM, Arns M, van Dongen-Boomsma M, Spronk D, Buitelaar JK (2011 ) The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency. Prog Neuropsychopharmacol Biol Psychiatry 35:47–52.

    Not in our library10.1016/j.pnpbp.2010.08.004

32. 32.
    An electrophysiological marker of arousal level in humans · 2020

    No notice
33. 33.

    Liechti MD, Valko L, Müller UC, Döhnert M, Drechsler R, Steinhausen HC, Brandeis D (2013 ) Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan. Brain Topogr 26:135–151.

    Not in our library10.1007/s10548-012-0258-6

34. 34.

    Yang L, Nan W, Qu X, Wan F, Mak PI, Mak PU, Vai MI, Hu Y, Rosa A (2015 ) Beta/theta ratio neurofeedback training effects on the spectral topography of EEG. Annu Int Conf IEEE Eng Med Biol Soc 2015:4741–4744.

    Not in our library10.1109/embc.2015.7319453

35. 35.

    Long CW, Shah NK, Loughlin C, Spydell J, Bedford RF (1989 ) A comparison of EEG determinants of near-awakening from isoflurane and fentanyl anesthesia: spectral edge, median power frequency, and δ ratio. Anesth Analg 69:169–173.

    Not in our library10.1213/00000539-198908000-00005

36. 36.

    Loo SK, Makeig S (2012 ) Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update. Neurotherapeutics 9:569–587.

    Not in our library10.1007/s13311-012-0131-z

37. 37.

    Lubar JF (1991 ) Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback Self Regul 16:201–225.

    Not in our library10.1007/bf01000016

38. 38.

    Matoušek M, Petersén I (1973 ) Automatic evaluation of EEG background activity by means of age-dependent EEG quotients. Electroencephalogr Clin Neurophysiol 35:603–612.

    Not in our library10.1016/0013-4694(73)90213-7

39. 39.

    Monastra VJ, Lubar JF, Linden M (2001 ) The development of a quantitative electroencephalographic scanning process for attention deficit-hyperactivity disorder: reliability and validity studies. Neuropsychology 15:136–144.

    Not in our library10.1037//0894-4105.15.1.136

40. 40.
    EEG UPPER/LOW ALPHA FREQUENCY POWER RATIO RELATES TO TEMPORO-PARIETAL BRAIN ATROPHY AND MEMORY PERFORMANCES IN MILD COGNITIVE IMPAIRMENT · 2013

    Not checked

Cited by papers we hold 1

• Quantification of stroke lesion volume using epidural EEG in a cerebral ischaemic rat model · 2021
  Not checked

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.