HIRREM™: a noninvasive, allostatic methodology for relaxation and auto-calibration of neural oscillations

Abstract

Disturbances of neural oscillation patterns have been reported with many disease states. We introduce methodology for HIRREM™ (high-resolution, relational, resonance-based electroencephalic mirroring), also known as Brainwave Optimization™, a noninvasive technology to facilitate relaxation and auto-calibration of neural oscillations. HIRREM is a precision-guided technology for allostatic therapeutics, intended to help the brain calibrate its own functional set points to optimize fitness. HIRREM technology collects electroencephalic data through two-channel recordings and delivers a series of audible musical tones in near real time. Choices of tone pitch and timing are made by mathematical algorithms, principally informed by the dominant frequency in successive instants of time, to permit resonance between neural oscillatory frequencies and the musical tones. Relaxation of neural oscillations through HIRREM appears to permit auto-calibration toward greater hemispheric symmetry and more optimized proportionation of regional spectral power. To illustrate an application of HIRREM, we present data from a randomized clinical trial of HIRREM as an intervention for insomnia (n = 19). On average, there was reduction of right-dominant temporal lobe high-frequency (23-36 Hz) EEG asymmetry over the course of eight successive HIRREM sessions. There was a trend for correlation between reduction of right temporal lobe dominance and magnitude of insomnia symptom reduction. Disturbances of neural oscillation have implications for both neuropsychiatric health and downstream peripheral (somatic) physiology. The possibility of noninvasive optimization for neural oscillatory set points through HIRREM suggests potentially multitudinous roles for this technology. Research is currently ongoing to further explore its potential applications and mechanisms of action.

Keywords: Allostasis; HIRREM; auto-calibration; biofeedback; electroencephalography; hemispheric asymmetry; insomnia; neural oscillation; relaxation; stochastic resonance.

Figure 1

Rhythmic output of a model system under conditions of health and fluctuating demand (A); “stuckness” due to prolonged or possibly acutely potent demand (B); pharmacotherapy (C); and an idealized health intervention, associated with gradual reduction of demand (D). Black vertical arrows denote changes in demand on the system. Adapted from Sterling (2004).

Figure 2

Allostatic model of blood pressure regulation (adapted from Sterling 2004).

Figure 3

Changing asymmetry at the bilateral temporal lobes over the course of five successive HIRREM exercises, in the 0–1 Hz (A) and 36–48 Hz (B) frequency bands. Yellow line represents amplitudes at T3, and red line represents amplitudes at T4. Each HIRREM exercise is represented by a background with alternating shades of gray.

Figure 4

Tukey plot of average asymmetry scores between T3 and T4 in the 23–36 Hz range, over the course of eight successive HIRREM exercises for 19 subjects enrolled in a clinical trial evaluating efficacy of HIRREM as an intervention for insomnia. Whiskers extend to last observation not more than 1.5 times the interquartile range. Dots represent observations more than 1.5 times the interquartile range.

Figure 5

Correlation between individual change in asymmetry between T3 and T4 in 23–36 Hz range over eight serial HIRREM exercises, and change in insomnia symptoms as measured by change in ISI score. More negative change in asymmetry scores indicates lesser dominance of amplitudes at T4 over T3. More negative ISI change scores indicate greater reduction in insomnia symptoms. Change in asymmetry is calculated as the slope of a linear equation fitted to the series of asymmetry scores over eight serial HIRREM exercises, for each individual.

Figure 6

High-frequency (23–36 Hz) amplitudes (microvolts) in bilateral temporal lobes (T3 yellow, T4 red), for a 37-year-old man with insomnia, obtained from continuous EEG recordings (eyes closed) while listening to 12 min of white noise (A), random musical tones (B), and musical tones generated from HIRREM software algorithms (C).