Science : QEEG guided rTMS

Background of QEEG guided intervention design

In a study looking at responsiveness to SSRIs used in MDD, those with a higher alpha theta ratio (ATR) in the prefrontal lobe responded better to SSRI, while those with a lower ATR responded better to dopamine+norepinephrine. (Leuchter et al., 2009) A similar study was first conducted in ADHD, where the frontal alpha excess group responded 87% to antidepressants and the frontal theta excess group responded 100% to stimulants.(S. C. Suffin & Emory, 1995) Even in MDD, 1 in 7 people with EEG-based medication selection showed improvement, but only 1 in 6 people without EEG-based medication selection showed clinical improvement.(Stephen C Suffin et al., 2007) The alpha increase in the frontal lobe appears to be ameliorated by serotonin, and the theta increase in the frontal lobe is ameliorated by dopamine supply, suggesting that the same interventions may be applicable to the same EEG phenotype for different diseases, such as depression and ADHD. Serotonin may affect alpha sources in the thalamo-cortico-thalamic loop to adjust anterior-posterior alpha gradient and dopamine may stimulate mesocortical neural circuits to decrease theta and increase beta. (Fingelkurts & Fingelkurts, 2022; Steriade, Gloor, Llinás, Lopes da Silva, & Mesulam, 1990)

In a study comparing STAR-D[1] based best prescriptions with prescriptions based on rEEG(reference EEG, EEG Phenotype compare to norm DB), patients who received rEEG prescriptions reported statistically significant greater improvement in symptoms from week 2 to week 12. (DeBattista et al., 2011) Ultimately, the need to utilize computational psychiatry was also suggested in Nature neuroscience. (Huys, Maia, & Frank, 2016) The basic idea is that EEG phenotypes can be an important basis for choosing the right medications for a patient, even in the absence of a diagnosis, and this is not just limited to medications, but is thought to extend to various neuromodulations.

A probable mechanism of QEEG guided neuromodulation

Pulsed neuromodualtion is characterized by entrainment at the specific pulse frequency in use. The mechanism of pulsed neuromodulation is not yet fully understood, but it can be analogized to the generation of EEG rhythms. Physiological EEG rhythms are produced by the firing of many synchronized pyramidal neurons, as in the case of the alpha rhythm –the most prominent rhythm– created by the simultaneous firing of neurons in the cortical area due to the pace-making of the thalamus. In neuromodulation methods, such as tACS, tPBM, and rTMS, it can be understood that the pulse frequency used acts as another pacemaker to the pyramidal neurons in the specific target area at the rhythm of the corresponding stimulation frequency. Regardless of the type of physical stimulus (current, magnetic, light, etc.), it appears that it is possible to speed up or slow down EEGs through voltage/light gated ion channels, depending on what frequency is used- driving EEG in a low-frequency direction– and high frequencies in a high-frequency direction, also called “entrainment” of brain oscillations. (Mathewson et al., 2012; Sameiro-Barbosa & Geiser, 2016) 

For example, a Parkinson's patient with tremor may have a predominantly fast EEG pattern, which can be improved by providing theta-tACS or 1 Hz inhibition tPBM, while a Parkinson's patient with rigid, fine motor skills may have a slower EEG pattern, which can be improved by providing beta-tACS or beta tPBM. (Del Felice et al., 2019) HF-rTMS and LF-rTMS have also been used in rTMS studies for depression, but there does not seem to be clear criteria for when to use HF and when to use LF. However, it seems that depression with fast EEG may require a slower tuning approach using LF, and depression with slow EEG may require a faster tuning approach using HF (Lefaucheur et al., 2020). There is an interesting study that repeatedly shows that the frequency of HF-rTMS can be adjusted according to the alpha peak frequency to maximize the therapeutic effect of rTMS for MDD, which shows that rTMS frequencies slightly faster than the individual's alpha peak frequency maximized the treatment effect for depression. In a study using 10 Hz rTMS, the further away from 10 Hz the alpha frequency was, the less effective the treatment was, with subjects with alpha frequencies around 9 Hz having the greatest benefit. (Roelofs et al., 2021) This also seems to be a good example of the entrainment or tuning effect of pulse frequency.

Implications of QEEG guided rTMS

Based on the above discussion, if we apply QEEG phenotypes to select a protocol for rTMS, we can distinguish between phenotypes that require activation and phenotypes that require inhibition according to the representative EEG phenotypes shown in the table(Johnstone, Gunkelman, & Lunt, 2005). Low volume fast, Epileptiform, Faster alpha variants, spindling excess beta phenotype will require inhibition, while Diffuse slow, Focal slow, excess temporal alpha, persistent eye open alpha, etc. will require activation.

QEEG will be able to provide a good guide for the selection of HF and LF in rTMS very cost-effectively, as well as the selection of frequencies and the target area. Although there are not many studies yet, it seems likely that this approach could provide patients with the benefits of precision medicine in neuromodualtion in the same way that it has been successful in medication selection.   

References

DeBattista, C., Kinrys, G., Hoffman, D., Goldstein, C., Zajecka, J., Kocsis, J., . . . Fava, M. (2011). The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression. J Psychiatr Res, 45(1), 64-75. doi:10.1016/j.jpsychires.2010.05.009

Del Felice, A., Castiglia, L., Formaggio, E., Cattelan, M., Scarpa, B., Manganotti, P., . . . Masiero, S. (2019). Personalized transcranial alternating current stimulation (tACS) and physical therapy to treat motor and cognitive symptoms in Parkinson's disease: A randomized cross-over trial. NeuroImage: Clinical, 22, 101768. doi:https://doi.org/10.1016/j.nicl.2019.101768

Fingelkurts, A. A., & Fingelkurts, A. A. (2022). Quantitative Electroencephalogram (qEEG) as a Natural and Non-Invasive Window into Living Brain and Mind in the Functional Continuum of Healthy and Pathological Conditions. Applied Sciences, 12(19), 9560. Retrieved from https://www.mdpi.com/2076-3417/12/19/9560

Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19(3), 404-413. doi:10.1038/nn.4238

Johnstone, J., Gunkelman, J., & Lunt, J. (2005). Clinical database development: characterization of EEG phenotypes. Clin EEG Neurosci, 36(2), 99-107. doi:10.1177/155005940503600209

Lefaucheur, J. P., Aleman, A., Baeken, C., Benninger, D. H., Brunelin, J., Di Lazzaro, V., . . . Ziemann, U. (2020). Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014-2018). Clin Neurophysiol, 131(2), 474-528. doi:10.1016/j.clinph.2019.11.002

Leuchter, A. F., Cook, I. A., Gilmer, W. S., Marangell, L. B., Burgoyne, K. S., Howland, R. H., . . . Greenwald, S. (2009). Effectiveness of a quantitative electroencephalographic biomarker for predicting differential response or remission with escitalopram and bupropion in major depressive disorder. Psychiatry Res, 169(2), 132-138. doi:10.1016/j.psychres.2009.04.004

Mathewson, K. E., Prudhomme, C., Fabiani, M., Beck, D. M., Lleras, A., & Gratton, G. (2012). Making waves in the stream of consciousness: entraining oscillations in EEG alpha and fluctuations in visual awareness with rhythmic visual stimulation. J Cogn Neurosci, 24(12), 2321-2333. doi:10.1162/jocn_a_00288

Roelofs, C. L., Krepel, N., Corlier, J., Carpenter, L. L., Fitzgerald, P. B., Daskalakis, Z. J., . . . Arns, M. (2021). Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium. Clin Neurophysiol, 132(2), 643-649. doi:10.1016/j.clinph.2020.10.017

Sameiro-Barbosa, C. M., & Geiser, E. (2016). Sensory Entrainment Mechanisms in Auditory Perception: Neural Synchronization Cortico-Striatal Activation. Frontiers in Neuroscience, 10. doi:10.3389/fnins.2016.00361

Steriade, M., Gloor, P., Llinás, R. R., Lopes da Silva, F. H., & Mesulam, M. M. (1990). Basic mechanisms of cerebral rhythmic activities. Electroencephalography and Clinical Neurophysiology, 76(6), 481-508. doi:https://doi.org/10.1016/0013-4694(90)90001-Z

Suffin, S. C., & Emory, W. H. (1995). Neurometric subgroups in attentional and affective disorders and their association with pharmacotherapeutic outcome. Clin Electroencephalogr, 26(2), 76-83. doi:10.1177/155005949502600204

Suffin, S. C., Emory, W. H., Gutierrez, N., Arora, G. S., Schiller, M. J., & Kling, A. (2007). A QEEG database method for predicting pharmacotherapeutic outcome in refractory major depressive disorders. Journal of American Physicians and Surgeons, 12(4), 104-109.

[1] Sequenced Treatment Alternatives to Relieve Depression (STAR*D) was a collaborative study on the treatment of depression, funded by the National Institute of Mental Health.