Light-Induced Guided Healing Therapy (LIGHT)

​Can a gentle visualization exercise positively transform health and overall well-being?

The Center has been collaborating on several research projects to examine the efficacy of Light-Induced Guided Healing Therapy (LIGHT) to improve health and wellbeing. LIGHT is a novel mind-body protocol that invokes and engages the undivided attention of a user to visualize the materialization of set goals by accessing one's awareness for the non-judgmental and exploration of possibilities.

LIGHT uses a combination toolset comprising of hypnosis and guided imagery forming the main scaffold for setting up the space suitable to introduce the proprietary script that invokes a set of favorable interactions with otherwise inaccessible or untapped realms of one's consciousness. In this environment, the person is able to modify adverse responses triggered by repressed anxiety or other subconscious beliefs that are triggered by stressors to then program mature and balanced approaches in its place that become the new 'go to' response.

Individuals practicing LIGHT have reported an increased awareness and growing ability to manage stressors with improved strategies and improved quality of life. Research on LIGHT suggests the same.  

Initial findings on LIGHT in individuals with a chronic autoimmune condition

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Initial findings published in the Journal for Evidence-Based Integrative Medicine (JEBIM), titled "Guided Imagery Improves Mood, Fatigue, and Quality of Life in Individuals With Multiple Sclerosis: An Exploratory Efficacy Trial", showed the following: a 75% decrease in depressed mood scores (vs. a 15% decrease with the control condition journaling);  a 24% decrease in fatigue scores (vs a 6% increase with journaling); a 38% increase in physical quality of life scores (vs a 3% increase with journaling); and a 30% increase in mental quality of life (vs 0% change with journaling) (in the publication LIGHT was then called HLGI) (

Abstract. Multiple sclerosis is a disabling and progressive neurological disease that has significant negative effects on health-related quality of life. This exploratory efficacy study examined the effects of Healing Light Guided Imagery (HLGI), a novel variant of guided imagery, compared with a wait-list control in patients with relapsing-remitting multiple sclerosis. Changes in the Beck Depression Inventory, Fatigue Severity Scale, and Multiple Sclerosis Quality of Life instrument (physical and mental components) were compared between groups. Patients who completed HLGI (N = 9) showed significant reductions in depressed mood ( P < .05) and fatigue ( P < .01) and showed significant gains in physical ( P = .01) and mental ( P < .01) quality of life compared with journaling (N = 8). Our results suggest that HLGI can improve self-reported physical and mental well-being in patients with relapsing-remitting multiple sclerosis. Further research is needed to study the effectiveness of this therapy, as well as its mind-body mechanisms of action.

Exploratory findings examining LIGHT and adaptive mixture independent component analysis of EEG

This study was in collaboration with the UCSD Swartz Center for Computational Neuroscience and published on ResearchGate (

Abstract. Advancing our understanding of neurocognitive systems impacted by hypnotherapy may improve therapeutic outcomes. This study addresses the challenge of decoding cortical state changes from continuous electroencephalographic (EEG) data recorded during hypnosis. We model changes in brain state dynamics over the course of hypnosis using Adaptive Mixture Independent Component Analysis (AMICA), an unsupervised approach that learns multiple ICA models for characterizing non-stationary, unlabeled data. Applied to EEG from six sessions of hypnosis, AMICA characterized changes in system-wide brain activity that corresponded to transitions between hypnosis stages. Moreover, the results showed consistent AMICA-based models across sessions and subjects that reflected distinct patterns of source activities in different hypnosis states. By analyzing in- dependent component clusters associated with distinctive classes of model probability patterns, shifts in the theta, alpha, and other spectral features of source activities were characterized over the course of the therapy sessions. The AMICA approach offers a promising tool for linking brain-network changes during hypnotherapy with physiological and cognitive state changes brought about by this form of treatment. It can also ignite new research and developments toward brain-state monitoring for clinical applications.

Excerpts. Guided imagery hypnotherapy (GIH) is a family of hypnotherapy techniques that involve bringing patients into a light, relaxation-based, self-hypnotic trance, then leading them through active visualization processes that support their therapy-related goals. It has been implicated in reducing anxiety, stress, and depression, as well as fatigue and pain in various clinical populations.

Once the model clusters were identified, we can assess the underlying brain networks and activities by examining independent components (ICs) that were consistently found across the AMICA models in each model cluster. Fig. 5plots the scalp topography and power spectra of the clusters of ICs in each model cluster corresponding to a dominant GIH stage. Notably, in the central and occipital regions, power in the theta (4 – 7 Hz) and alpha (8 – 12 Hz) range declines during post-session relative to pre-session rest baseline. A similar trend can be observed over the course of hypnosis as well – that is, whereas the induction and early visualization stages are characterized by broadly distributed, pronounced peaks in the alpha and alpha harmonic ranges, little indication of these activities is discernible as the visualization process advances, in keeping with the idea that a broad network of brain systems became increasingly engaged as individuals progressed through the visualization sequence.

Further studies are being planned to examine the durability of effects as well as effects on measures of disease–specific activity with more standard active control groups that incorporate relevant biomarkers and disease activity outcomes to further advance this work.