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A Deep Dive Into the Theta Rhythm

Updated: Jun 26


EEG rhythms


Theta Rhythm Overview


The theta rhythm ranges from 3-7 Hz, 4-7 Hz, or 4-8 Hz with 20-100 µV (Thompson & Thompson, 2015). Theta may be arrhythmic or rhythmic (Demos, 2019). Theta is seen during drowsiness or starting to sleep, hypnagogic imagery (intense imagery experienced before sleep onset), and hypnosis (Libenson, 2024). The greatest amplitude is found in the frontal and temporal regions of the scalp. Since several theta generators may exist, the theta rhythm is associated with different behavioral processes. The theta rhythm is associated with creativity but also with anxiety, daydreaming, depression, inattention, and minor TBI. Excessive left hemisphere (LH) theta may be associated with depression and right hemisphere (RH) theta may be linked to anxiety (Demos, 2019).


EEG activity in the theta frequency band is quite specific to the location where it is recorded. 4-8 Hz activity in temporal areas has different functional and behavioral correlates from the same frequency activity in frontal midline or posterior areas. This, again, reaffirms that location and behavior are essential components when analyzing scalp EEG.


Below is an example of filtered (4-8 Hz) theta activity.

theta
Caption: This is an eyes-closed recording in the longitudinal bipolar montage with a 20-µV scale with dark vertical lines showing the beginning of each new 1-second segment. The EEG is displayed using a 4-8 Hz filter to isolate that frequency band from the full band EEG. Observe the slightly greater amplitude and rhythmicity in temporal derivations.

Below is a 1-45 Hz display of the same EEG recording at the same time location.

theta
Caption: This is an eyes-closed recording in the longitudinal bipolar montage with a 50-µV scale. The EEG is displayed using a 1-45 Hz filter to show the relatively full EEG band.


Theta activity in an awake adult is considered abnormal (Libenson, 2024).


Amzica and Lopes da Silva (2018) cite various studies regarding the theta rhythm, which they identify as 4-7 Hz. As discussed earlier, they note that normal theta activity should not be confused with pathologic theta, which represents a slowing of the alpha frequency band into the theta range. They suggest that this slowing of alpha may result from reducing cerebral blood flow or metabolic encephalopathies. Metabolic encephalopathies can result from chemical imbalances due to various causal factors, from kidney or liver dysfunction, diabetes, or a variety of other health issues.


Arnolds et al. (1980) found significant differences between behavioral conditions when viewing hippocampal theta recorded with depth electrodes. Writing resulted in faster frequency and greater rhythmicity but lower amplitude than sitting or walking. In contrast, a word association task resulted in faster frequency, greater rhythmicity, and increased amplitude in the period of silence immediately following the question but before the answer was given.


Ekstrom and colleagues (2005) studied hippocampal and neocortical theta activity during a virtual driving task that involved location finding. They found that both areas increased theta during all tasks associated with the driving simulation. A significant correlation between all areas showed increased coordination between multiple areas while accomplishing the tasks.  They concluded that cortical and hippocampal theta oscillations and coordination between these areas are associated with attention and sensorimotor integration.



Childhood Disorders


The theta rhythm is the dominant frequency in healthy young children (Thompson & Thompson, 2015). Theta amplitudes and normative theta-to-beta ratios are higher in children than in older adults. Children diagnosed with ADHD often have higher ratios than children without ADHD. Theta-to-beta ratios greater than 3:1 may indicate a slow-wave disorder, and children with a slow-wave disorder may have ratios as high as 6:1 (Demos, 2019). Excessive theta graphic retrieved from ADDYSSEY.



Historically, the study of attention disorders has focused on excess frontal theta activity in individuals with inattentive ADHD. The ratio of theta (4-8 Hz) activity to beta (13-21 Hz) activity, or the theta/beta ratio (T/B ratio), was developed to make the analysis of this metric easier. It was initially calculated using a single channel vertex location at Cz (Monastra et al., 1999). Other studies compared multiple locations and found that the Cz location was accurate and represented the location of the largest deviation of the ratio between previously diagnosed ADHD clients and typical controls (Lubar, 1991). Identifying an elevated T/B ratio (meaning more than typical theta compared to the amount of beta) compared to typical controls appeared to be an accurate way to assess attention disorders.


In a large, blinded, multi-center validation of the theta/beta ratio compared to rating scales for assessing ADHD, the researchers calculated the sensitivity and specificity of measures. They found that the T/B ratio achieved superior results than commonly used rating scales (Snyder et al., 2008). With a sample size of 159 individuals, the EEG assessment showed a sensitivity of 87% and a specificity of 94%, for an overall accuracy of 89% compared to the next closest measure, the Connors’ Rating Scale – Teacher version (CRS – Teacher), with a sensitivity of 67% and specificity of 41%, for an overall accuracy of 58%.


The sensitivity of an assessment measure determines how accurately it identifies individuals known to have a particular condition. Specificity measures how accurately the measure correctly eliminates individuals without the condition from being identified as having the condition.


For example, the Conners Parent Rating Scale-Revised (CPRS-R) shows a sensitivity of 77%, correctly identifying 77 percent of clients with ADHD. It has a specificity of 73%, correctly identifying 73 percent of clients known not to have ADHD. The theta/beta ratio biomarker described above outperforms the CPRS-R by correctly identifying more true positive ADHD and negative non-ADHD clients (Chang et al., 2016).


However, Chang and colleagues note that the Child Behavior Checklist (CBCL), also included in their study, provides a more comprehensive analysis of the client and is more effective at identifying possible comorbidities often mistaken for the different subtypes of ADHD. They also note that using such a checklist approach provides the clinician with information they may not otherwise be able to identify in the clinical setting. It appears that a combined approach using a well-validated checklist in combination with objective measures such as the theta/beta ratio or other quantitative EEG assessment tools and a continuous performance test such as the Test of Variables of Attention (TOVA) would provide a comprehensive assessment for childhood disorders of attention.


In the research of Snyder and colleagues (2008), only 6% of typical children were incorrectly identified using the T/B ratio. This suggests that, at a minimum, a simple, single-channel EEG assessment should be included as a component of a comprehensive approach to identifying ADHD children before prescribing medication.


Interestingly, Van Son and colleagues (2019) expanded the associations that could be identified with the T/B ratio. They determined that a higher T/B ratio was negatively correlated with prefrontal executive control, including response inhibition and negative affect control. This suggests that excess theta in relation to beta activity could lead to greater impulsivity and to a lack of control of negative behaviors. They also found an association between higher T/B ratios and reward-motivated decision-making, possibly selecting immediate gratification at the expense of long-term benefit. Finally, they correlated the T/B ratio with increased mind wandering, decreased executive network functions, and increased default mode network (DMN) activity.


As with all the previous EEG frequencies and assessment measures, the correct amount of a particular frequency activity is important. For example, someone who lacks appropriate default mode functioning may experience a lack of the type of resting-state activity that appears to have a therapeutic effect. The DMN has also been called the resting state network (RSN) due to its functions that differ from task-oriented behaviors. It is thought to be important for a variety of reasons. Therefore, a person with a lower-than-typical T/B ratio may benefit from increased theta voltage and some training in activating the DMN. In contrast, an individual diagnosed with ADHD may benefit from training to reduce or inhibit excess theta voltage.


Temporal lobe theta likely reflects the hippocampal theta identified using depth electrodes. It appears to be associated with route finding and navigation, both hippocampal functions. Differential, interhemispheric (bipolar montage) training of temporal lobe areas in the theta frequency range has been a component of certain approaches to neurofeedback for some time (Othmer, 2007). This approach is used for various conditions, including migraine, tinnitus, PMS, and many others. Frequencies are adjusted to facilitate the optimal response and may range from the alpha frequencies through the theta frequencies down to the infra-low frequencies below 1 Hz.


Again, the analysis of theta activity is often aided by using a normative database. Otherwise, determining whether an amount is too high or too low in amplitude is difficult. Fortunately, for the T/B ratio assessment, Monastra (2001) has provided a table of values with three age ranges: 6-11 yrs, 12-15 yrs, and 16-20 yrs. The table of mean T/B power ratios is below.

theta-beta power
Caption: ADHD-I = attention deficit-hyperactivity disorder, inattentive type; ADHD-H/C = attention deficit-hyperactivity disorder, hyperactive-combined type.

Assessment of theta activity, more generally, beyond the T/B ratio in the central midline, is more challenging. As van Son (2019) noted, assessment under task may be essential to determine these results more accurately.


Two strategies to reduce high theta-to-beta ratios are amplitude training (down-training theta) and ratio training (rewarding decreases in the theta-to-beta ratio; Demos, 2019).



Cognitive Decline


Brief memory lapses (senior moments) are associated with LH bursts of rhythmic temporal theta (BORTTs) due to sleepiness or reduced hippocampal perfusion. visual inspection of the analog EEG record. Demos (2019) recommends using memory challenges such as a computerized card-matching game for assessment and training when such morphology is observed. Neurofeedback training to inhibit theta at T3 may be done with eyes closed or eyes open while performing a cognitive task such as attending to video graphics or reading (Demos, 2019, p. 70).


While alpha/theta protocols to treat substance use disorders up-train theta, this should be proscribed in epilepsy in the frontal lobes, where it could impair attention or decisions, or in PSTD due to the risk of provoking flashbacks (Demos, 2019).


The movie below is a 19-channel BioTrace+ /NeXus-32 display of theta activity © John S. Anderson. Brighter colors represent higher theta amplitudes. Frequency histograms are displayed for each channel.




Substance Use Disorders


While alpha/theta protocols to treat substance use disorders up-train theta, this should be proscribed in epilepsy in the frontal lobes, where it could impair attention or decisions, or in PSTD due to the risk of provoking flashbacks (Demos, 2019). Using alpha-theta training protocols has generated caution from some practitioners and instructors, as indicated above in Demos (2019) and in conference presentations (personal experience). These cautions may result from a misunderstanding of the mechanism of alpha-theta training. In an upcoming post, John S. Anderson will address some of these issues and questions.



Final Notes


Recognition of standard EEG frequencies, analysis of such activity, and use of this information for training and re-assessment are important parts of neurofeedback practice. This section has attempted to provide an overview of this area to aid new practitioners and experienced clinicians in furthering their understanding of this complex study area.


One of the greatest benefits of neuroscience in general and electroencephalography and neurofeedback specifically is that they encourage lifelong learning. Suppose this section appeared overwhelming, with few hard and fast rules or concrete facts to hold on to. In that case, it is important to remember that one can do useful and effective neurofeedback without an in-depth understanding of this area. Understanding the EEG comes gradually through regular exposure to educational materials, lectures, and workshops, working with an experienced mentor, and regular interaction with clients and their EEG recordings. Pursuing and maintaining certification with the Biofeedback Certification International Alliance (BCIA) is a good way to engage in continuing professional education and lifelong learning.


There is no substitute for viewing large numbers of EEG recordings. As mentioned in the beginning, this can start with an EEG atlas and then progress to examining your own recordings, whether a single channel, a couple of channels, or multiple channels. 


Patience with one’s process is an integral part of any learning experience. Think of the time it takes to learn any worthwhile skill, from swimming to learning a musical instrument to learning a new computer or phone operating system. Managing one’s expectations is crucial.



Glossary

bursts of rhythmic temporal theta (BORTTs): transient periods of rhythmic theta activity observed in the temporal regions of the brain during EEG recording. These bursts typically manifest as short-lived increases in theta oscillations and are often associated with cognitive processes such as memory encoding and retrieval.

theta/beta ratio: the ratio between 4-7 Hz theta and 13-21 Hz beta, measured as amplitude squared, most typically along the midline and generally in the anterior midline near the 10-20 system location Fz.


theta rhythm: 4-8-Hz rhythms generated by a cholinergic septohippocampal system that receives input from the ascending reticular formation and a noncholinergic system that originates in the entorhinal cortex, which corresponds to Brodmann areas 28 and 34 at the caudal region of the temporal lobe.



References


Amzica, F., & Lopes da Silva, F. H. (2018). Cellular substrates of brain rhythms. In D. L. Schomer & F. H. Lopes da Silva (Eds.), Niedermeyer’s electroencephalography: Basic principles, clinical applications and related fields (7th ed., pp. 20–62). Oxford University Press.

Arnolds, D. E., Lopes da Silva, F. H., Aitink, J. W., Kamp, A., & Boeijinga, P. (1980). The spectral properties of hippocampal EEG related to behaviour in man. Electroencephalography and Clinical Neurophysiology, 50(3-4), 324–328. https://doi.org/10.1016/0013-4694(80)90160-1 Chang, L. Y., Wang, M. Y., & Tsai, P. S. (2016). Diagnostic accuracy of rating scales for Attention-Deficit/Hyperactivity Disorder: A meta-analysis. Pediatrics, 137(3), e20152749. https://doi.org/10.1542/peds.2015-2749 Demos, J. N. (2019). Getting started with neurofeedback (2nd ed.). W. W. Norton & Company.

Ekstrom, A. D., Caplan, J. B., Ho, E., Shattuck, K., Fried, I., & Kahana, M. J. (2005). Human hippocampal theta activity during virtual navigation. Hippocampus, 15(7), 881–889. https://doi.org/10.1002/hipo.20109 Libenson, M. H. (2024). Practical approach to electroencephalography (2nd ed.). Elsevier. Lubar, J. F. (1991). Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback and Self-regulation, 16(3), 201–225. https://doi.org/10.1007/BF01000016 Monastra, V. J., Lubar, J. F., & Linden, M. (2001). The development of a quantitative electroencephalographic scanning process for attention deficit-hyperactivity disorder: Reliability and validity studies. Neuropsychology, 15(1), 136–144. https://doi.org/10.1037//0894-4105.15.1.136 Monastra, V. J., Lubar, J. F., Linden, M., VanDeusen, P., Green, G., Wing, W., Phillips, A., & Fenger, T. N. (1999). Assessing attention deficit hyperactivity disorder via quantitative electroencephalography: an initial validation study. Neuropsychology, 13(3), 424–433. https://doi.org/10.1037/0894-4105.13.3.424 Othmer S. ( 2007). Progress in neurofeedback for the autism spectrum. Paper presented at the 38th Annual Meeting of the Association for Applied Psychophysiology & Biofeedback Monterey, Canada, 15–18 February 2007. Snyder, S. M., Quintana, H., Sexson, S. B., Knott, P., Haque, A. F., & Reynolds, D. A. (2008). Blinded, multi-center validation of EEG and rating scales in identifying ADHD within a clinical sample. Psychiatry Research, 159(3), 346–358. https://doi.org/10.1016/j.psychres.2007.05.006

Thompson, M., & Thompson, L. (2015). The biofeedback book: An introduction to basic concepts in applied psychophysiology (2nd ed.). Association for Applied Psychophysiology and Biofeedback. van Son, D., de Rover, M., De Blasio, F. M., van der Does, W., Barry, R. J., & Putman, P. (2019). Electroencephalography theta/beta ratio covaries with mind wandering and functional connectivity in the executive control network. Annals of the New York Academy of Sciences, 1452(1), 52–64. https://doi.org/10.1111/nyas.14180



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