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A Deep Dive into the Sensorimotor Rhythm

Updated: Jun 25


SMR rhythm


SMR activity falls within the frequency range of 12 to 15 Hz, overlapping with the beta band's lower and alpha bands' higher ends. It is typically recorded over the sensorimotor cortex and characterized by rhythmic oscillations distinct from the higher-frequency beta and lower-frequency alpha waves (Sterman, 1996).


Sensorimotor Rhythm Generators


The primary generators of SMR activity are the neurons in the sensorimotor cortex, particularly those involved in motor control and sensory processing.


The SMR rhythm is generated by thalamic ventrobasal relay cells and reentrant thalamocortical loops (Thompson & Thompson, 2015). These circuits involve the thalamus sending sensory information to the cortex, where it is processed and sent back to the thalamus, creating rhythmic oscillations (Lopes da Silva, 1991).


The sensorimotor cortex, particularly the primary motor cortex (M1) and the primary somatosensory cortex (S1), contains neurons that contribute to the generation of SMR. The synchronized activity of these neurons results in the rhythmic patterns observed in SMR (Pfurtscheller & Lopes da Silva, 1999).


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






The Meaning of the Sensorimotor Rhythm


SMR activity is associated with a state of relaxed wakefulness and motor inhibition. The sensorimotor rhythm may signal an internal focus (Demos, 2019). It is believed to reflect the brain's ability to maintain a calm, focused state without engaging in motor activity. SMR is often considered a marker of optimal sensorimotor integration linked to several cognitive and motor functions.


SMR is related to the suppression of motor activity, indicating a state where the brain is inhibiting unnecessary movements. This is important for stillness and precision tasks (Sterman, 1996). Increased SMR activity is associated with improved motor performance and reduced motor activity (Corsi-Cabrera et al., 2001; Sazgar & Young, 2019).


Increased SMR activity is associated with improved attention and focus, as it reflects a state where the brain is not distracted by extraneous motor activity and instead concentrates on sensory input and cognitive tasks (Egner & Gruzelier, 2001).



Psychological and Medical Disorders


ADHD


Individuals with ADHD often exhibit reduced SMR activity. Neurofeedback training to increase SMR can improve attention and behavioral control (Lubar, 1991). Based on six RCTs, Stefanie Enriquez-Geppert and colleagues (2023) rated neurofeedback for ADHD as efficacious and specific in Evidence-Based Practice in Biofeedback and Neurofeedback (4th ed.).



Generalized Seizures


Abnormal SMR activity can be indicative of dysfunctional thalamocortical circuits, which are often implicated in the generation of epileptic seizures. Modulating SMR activity through neurofeedback or other interventions has been explored as a potential therapeutic approach for reducing seizure frequency and severity (Moffet et al., 2017).


Frey (2023) rated SMR-based and slow cortical potential (SCP)-based neurofeedback as efficacious for seizures, SMR-based NFB as probably efficacious for non-seizure manifestations of epilepsy, and connectivity-based NFB as not empirically supported for seizures.


In two meta-analyses (Soroush et al., 2023; Tan et al., 2009), SMR- and SCP-based treatments were associated with fewer seizures. In a sham-controlled RCT, neurofeedback for pediatric focal epilepsy was associated with improved quality of life, but only SMR neurofeedback improved cognition (Morales-Quezada et al., 2019).



Anxiety and Depression


SMR neurofeedback has also been explored as a treatment for anxiety and depression. Increasing SMR activity can help alleviate symptoms by promoting a state of calm and reducing hyperarousal (Hammond, 2005).



Sleep


During sleep, particularly in non-rapid eye movement (NREM) sleep, SMR activity is less prominent than during wakefulness. However, the broader beta band, which includes SMR frequencies, shows distinct patterns across different sleep stages. For instance, beta activity is generally reduced during NREM sleep but can show transient increases during sleep spindles, which are bursts of oscillatory brain activity during stage 2 NREM sleep (Krystal et al., 2002; Merica & Fortune, 2005).


SMR activity plays a role in sleep regulation and quality. Higher levels of SMR activity are associated with better sleep onset and maintenance. Individuals with higher SMR activity tend to fall asleep more easily and have more stable sleep patterns (Hoedlmoser et al., 2008). SMR neurofeedback training has been shown to improve sleep quality by increasing SMR activity, leading to deeper and more restorative sleep (Cortoos et al., 2010).




Performance


Enhancing SMR activity through neurofeedback training has been shown to improve cognitive performance, including memory, attention, and executive function. This is likely due to the increased focus and reduced distractibility associated with higher SMR levels (Vernon et al., 2003).


Increased SMR activity is associated with improved motor performance, particularly in tasks requiring fine motor control and precision. This is because SMR reflects a state of motor inhibition, allowing for more controlled and deliberate movements (Gruzelier et al., 2014).


The Sensorimotor Rhythm and Sleep Spindles


Sleep spindles and sensory-motor rhythm (SMR) share several characteristics and underlying mechanisms, highlighting their interconnected roles in brain function, particularly in relation to sleep. The sleep spindle graphic © eegatlas-online.com.


sleep spindle


Similarities and Overlapping Features


Both SMR and sleep spindles operate within overlapping frequency ranges. SMR typically ranges from 12 to 15 Hz, while sleep spindles are generally observed in the 12 to 16 Hz range. This overlap suggests a possible functional and mechanistic connection between the two types of brain activity (De Gennaro & Ferrara, 2003; Sterman, 1996).


Thalamocortical circuits generate both SMR and sleep spindles. The thalamus, particularly the thalamic reticular nucleus, is crucial in generating sleep spindles by interacting with the cortex to produce rhythmic bursts of activity. Similarly, the thalamocortical loops involved in SMR generation facilitate synchronized oscillations in the sensorimotor cortex (Steriade et al., 1993; Lopes da Silva, 1991).


SMR is associated with relaxed wakefulness and motor inhibition, which can facilitate the transition to sleep. Sleep spindles occur during stage 2 of non-REM sleep and are associated with maintaining sleep, particularly by protecting the sleeper from external stimuli and aiding sleep consolidation (De Gennaro & Ferrara, 2003; Hammond, 2005).



Functional Roles


Sleep spindles play a critical role in maintaining sleep stability by reducing the brain’s responsiveness to external stimuli. SMR, by promoting a calm and relaxed state, may facilitate the onset of sleep and enhance the stability of the sleep cycle, thereby supporting the generation of sleep spindles (Hoedlmoser et al., 2008).


SMR and sleep spindles are implicated in memory consolidation processes. Sleep spindles are known to be involved in consolidating declarative and procedural memories during sleep. SMR, by supporting focused attention and cognitive control during wakefulness, may indirectly enhance memory processes by optimizing brain function before sleep (Marshall & Born, 2007; Vernon et al., 2003).


Neurofeedback training targeting SMR has been shown to improve sleep quality and cognitive performance. Similarly, interventions aimed at enhancing sleep spindle activity have been explored for their potential to improve memory and cognitive function. The shared thalamocortical mechanisms suggest that training in one of these rhythms could potentially influence the other, offering combined benefits for sleep and cognitive health (Cortoos et al., 2010; Hoedlmoser et al., 2008).



Summary


SMR EEG activity, occurring in the frequency range of 12 to 15 Hz, is primarily generated by neurons in the sensorimotor cortex, particularly those involved in motor control and sensory processing. Thalamic ventrobasal relay cells and reentrant thalamocortical loops are crucial in generating SMR. The thalamus sends sensory information to the cortex, which processes it and sends it back to the thalamus, creating rhythmic oscillations. The synchronized activity of neurons in the primary motor cortex (M1) and primary somatosensory cortex (S1) results in the rhythmic patterns observed in SMR.


SMR activity is associated with relaxed wakefulness and motor inhibition, reflecting the brain's ability to maintain a calm, focused state without engaging in motor activity. It is considered a marker of optimal sensorimotor integration linked to several cognitive and motor functions, such as improved attention, focus, and motor performance.


In psychological and medical disorders, SMR activity is often reduced in individuals with ADHD, but neurofeedback training can improve attention and behavioral control. Abnormal SMR activity is also implicated in generalized seizures, anxiety, and depression, with neurofeedback training showing potential therapeutic benefits.


SMR plays a role in sleep regulation and quality. Higher SMR levels are associated with better sleep onset and maintenance, and SMR neurofeedback training has been shown to improve sleep quality, leading to deeper and more restorative sleep.


Enhancing SMR activity through neurofeedback training improves cognitive performance, including memory, attention, and executive function, as well as motor performance, particularly in tasks requiring fine motor control and precision.


SMR and sleep spindles share several characteristics and underlying mechanisms, particularly their generation by thalamocortical circuits. Both play critical roles in maintaining sleep stability and are involved in memory consolidation processes. Neurofeedback training targeting SMR can improve sleep quality and cognitive performance, and interventions to enhance sleep spindle activity show similar benefits.



Glossary


anxiety: a mental health disorder characterized by feelings of worry, anxiety, or fear that are strong enough to interfere with daily activities. attention: the cognitive process of selectively concentrating on one aspect of the environment while ignoring others. attention-deficit hyperactivity disorder (ADHD): a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity.

beta band: a frequency range in EEG activity typically between 12 and 30 Hz, associated with active, alert, and engaged states.

cognitive performance: the ability to use mental processes to perform tasks, including memory, attention, and executive function.

fine motor control: the ability to make small, precise movements, often involving the coordination of muscles and nerves.

hyperarousal: a state of increased psychological and physiological tension, often associated with anxiety and stress.

motor inhibition: suppressing motor activity, allowing for stillness and precision in movements.

non-rapid eye movement (NREM) sleep: non-rapid eye movement sleep, which includes stages 1-3 and is characterized by slower brain waves.

primary motor cortex (M1): a brain region involved in planning, controlling, and executing voluntary movements.

primary somatosensory cortex (S1): a brain region responsible for processing sensory information from the body.

relaxed wakefulness: a state of being awake but calm and free from active motor activity.

sensorimotor cortex: a region of the brain that processes and integrates sensory inputs and motor outputs. sensorimotor rhythm (SMR) activity: 12 to 15 Hz oscillations associated with motor inhibition and focused attention.

sleep quality: a measure of how well one sleeps, including aspects such as sleep onset, maintenance, and restorative value.

sleep spindles: bursts of oscillatory brain activity observed during stage 2 of NREM sleep, associated with sleep stability and memory consolidation. slow cortical potentials: gradual voltage changes in the EEG that reflect changes in cortical excitability and are associated with various cognitive and motor processes. thalamic ventrobasal relay cells: neurons in the thalamus that transmit sensory information from the body to the cortex, playing a crucial role in sensory processing and motor control.

thalamocortical circuits: neural pathways that connect the thalamus with the cortex, which generate rhythmic brain activity.



References


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Cortoos, A., De Valck, E., Arns, M., Breteler, M. H., & Cluydts, R. (2010). An exploratory study on the effects of tele-neurofeedback and tele-biofeedback on objective and subjective sleep in patients with primary insomnia. Applied Psychophysiology and Biofeedback, 35(2), 125-134. https://doi.org/10.1007/s10484-009-9116-z

De Gennaro, L., & Ferrara, M. (2003). Sleep spindles: An overview. Sleep Medicine Reviews, 7(5), 423-440. https://doi.org/10.1053/smrv.2002.0252 Demos, J. N. (2019). Getting started with neurofeedback (2nd ed.). W. W. Norton & Company.

Egner, T., & Gruzelier, J. H. (2001). Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans. NeuroReport, 12(18), 4155-4159. https://doi.org/10.1097/00001756-200112210-00058

Enriquez-Geppert, S., Brown, T., Henrich, H., Arns, M., & Pimenta, M. G. (2023). Attention Deficit Hyperactivity Disorder. In I. Khazan, F. Shaffer, D. Moss, R. Lyle, & S. Rosenthal (Eds). Evidence-based practice in biofeedback and neurofeedback (4th ed.). Association for Applied Psychophysiology and Biofeedback. Frey, L. (2023). Epilepsy. In I. Khazan, F. Shaffer, D. Moss, R. Lyle, & S. Rosenthal (Eds). Evidence-based practice in biofeedback and neurofeedback (4th ed.). Association for Applied Psychophysiology and Biofeedback.

Gruzelier, J. H., Inoue, A., Smart, R. G., Steed, A., & Steffert, T. (2014). Acting performance and flow state enhanced with sensory-motor rhythm neurofeedback comparing ecologically valid immersive VR and training screen scenarios. Neuroscience Letters, 480(2), 112-116. https://doi.org/10.1016/j.neulet.2014.04.027


Hammond, D. C. (2005). Neurofeedback with anxiety and affective disorders. Child and Adolescent Psychiatric Clinics of North America, 14(1), 105-123. https://doi.org/10.1016/j.chc.2004.07.008


Hoedlmoser, K., Pecherstorfer, T., Gruber, G., Anderer, P., Doppelmayr, M., Klimesch, W., & Schabus, M. (2008). Instrumental conditioning of human sensorimotor rhythm (12-15 Hz) and its impact on sleep as well as declarative learning. Sleep, 31(10), 1401–1408.


Lopes da Silva, F. H. (1991). Neural mechanisms underlying brain waves: from neural membranes to networks. Electroencephalography and Clinical Neurophysiology, 79(2), 81-93. https://doi.org/10.1016/0013-4694(91)90044-5


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

Merica, H., & Fortune, R. (2005). Spectral power time-courses of human sleep EEG reveal a striking discontinuity at approximately 18 Hz marking the division between NREM-specific and wake/REM-specific fast frequency activity. Cerebral Cortex, 15(7), 877-884. Moffett, S., O’Malley, S., Man, S., Hong, D., & Martin, J. (2017). Dynamics of high frequency brain activity. Scientific Reports, 7. https://doi.org/10.1038/s41598-017-15966-6. Morales-Quezada, L., Martinez, D., El-Hagrassy, M. M., Kaptchuk, T. J., Sterman, M. B., & Yeh, G. Y. (2019). Neurofeedback impacts cognition and quality of life in pediatric focal epilepsy: An exploratory randomized double-blinded sham-controlled trial. Epilepsy & Behavior: E&B, 101(Pt A), 106570. https://doi.org/10.1016/j.yebeh.2019.106570

Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology, 110(11), 1842-1857. https://doi.org/10.1016/S1388-2457(99)00141-8

Sazgar, M., & Young, M. (2019). Normal EEG awake and sleep. Absolute Epilepsy and EEG Rotation Review. https://doi.org/10.1007/978-3-030-03511-2_6 Soroush-Vala, A., Rahmanian, M., Jadid, M., & Hassanvandi, S. (2023). Application of neurofeedback in treating epilepsy: A systematic review and meta-analysis. International Journal of Body, Mind and Culture, 10, 143-157. https://doi.org/10.22122/ijbmc.v10i2.506 Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993). Thalamocortical oscillations in the sleeping and aroused brain. Science, 262(5134), 679-685. https://doi.org/10.1126/science.8235588

Sterman, M. B. (1996). Physiological origins and functional correlates of EEG rhythmic activities: implications for self-regulation. Biofeedback and Self-regulation, 21(1), 3-33. https://doi.org/10.1007/BF02214146


Sterman, M. B. (2000). Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning. Clinical Electroencephalography, 31(1), 45-55. https://doi.org/10.1177/155005940003100111

Tan, G., Thornby, J., Hammond, D. C., Strehl, U., Canady, B., Arnemann, K., & Kaiser, D. A. (2009). Meta-analysis of EEG biofeedback in treating epilepsy. Clinical EEG and Neuroscience, 40(3), 173–179. https://doi.org/10.1177/155005940904000310 Thompson, M., & Thompson, L. (2015). The neurofeedback book: An introduction to basic concepts in applied psychophysiology (2nd ed.). Association for Applied Psychophysiology and Biofeedback.

Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C., Sheri, A., & Gruzelier, J. (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. International Journal of Psychophysiology, 47(1), 75-85. https://doi.org/10.1016/S0167-8760(02)00091-0




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