**Rationale**: Studies conducted by us and other investigators have shown that biofield therapy has the potential to influence tumor biology and resulted in suppression of the growth both in vitro cancer cells and in vivo tumor models. However, it is not clear how biofield therapy may impact tumor growth because any measurements of electromagnetic fields or biophoton emissions from the biofield therapist has not been measured. This experiment will determine if biofield therapy has a direct impact on mouse brain function in real-time. This will ultimately help in determining the mechanisms of biofield therapy. We will also explore any potential coherence between the therapist and mouse brain function over time. For this, we will compute connectivity measures across all pairs of electrodes of the human and mouse participants to assess significant connection between the two brains. In addition, we will be able to compare brain function between mice with and without cancer. This will be done at the first baseline mouse EEG reading before the start of biofield therapy. Through this last question we will learn a lot about how cancer alone can change brain function and be linked with cancer growth. **Animal model**: We will use the similar mouse liver cancer model that we plan to use in the in vivo antitumor efficacy studies. TRE-MYC/FVB mice are crossed with LAP-tTA/mice, backcrossed with FVB mice for more than 10 generations in our lab. When male mice are 8 wks old, doxycycline will be removed from drinking water. The C-Myc mice and wild-type (no cancer) mice will then be transferred to Baylor. After a 14-day acclamation period all mice will have surgery to implant the EEG cap according the procedure described below. After 2-week recovery the cancers in the C-Myc mice will have formed and biofield therapy can begin. **Study Design**: The study will use a 2 x 2 design of cancer (yes/no) and biofield therapy (yes/no). LPA-tTA/TRE-Myc mice (n = 16) and its wildtype littermate (n = 16) will be randomized to either sham or biofield therapy (n = 8 animals in each of the 4 groups). Animals will then be exposed to 3 separate days of biofield therapy or sham therapy with EEG collected from the mice and biofield therapists. After the EEG begins, the therapist will signal the onset of what he determines to be the start of the treatment session. An event marker will be placed in the EEG. As appropriate, the therapist will signal when the connection is at maximum and any fluctuations. Event markers will be placed for each occurrence of increase or decrease in connection. The therapist will then stop the treatment process and another event marker will delineate that the treatment period is over. In addition to EEG measurement, we will also assess tumor growth comparing treatment versus controls. We will also collect and freeze blood samples, tumors, and brain tissue for potential future assays. *(Figure 1. Effect of biofield therapy on the EEG in a mouse model of cancer)*![enter image description here][1] **Mouse EEG recording**: Mice will be secured on a stereotaxic frame (David Kopf) under 1–2% isoflurane anesthesia. Each mouse will be prepared under aseptic condition for the following recordings: Teflon-coated silver wires (bare diameter 127 mm, A-M systems) will be implanted bilaterally in the subdural space of the somatosensory cortex45 (0.8 mm posterior, 1.8 mm lateral to bregma) with reference to the midline over the cerebellum) for cortical EEG as well as in neck muscles for electromyogram recordings to monitor mouse activity. An additional electrode constructed with Teflon-coated tungsten wire (bare diameter 50 mm, A-M systems) will be stereo-taxically implanted in the CA1 of the hippocampus (1.9 mm posterior, 1.0 mm lateral, and 1.3 mm ventral to bregma) with reference to the ipsilateral corpus callosum for local field potential recordings. All electrode wires and the attached miniature connector sockets will be fixed to the skull by dental cement. After 2 weeks of post-surgical recovery and prior to the treatment, mice will receive Phase I video-EEG recordings (30 min – 2 hr). Signals will be amplified (100x) and filtered (bandpass, 0.1 Hz - 1 kHz) by 1700 Differential AC Amplifier (A-M Systems), then digitized at 2 kHz and stored on disk for off-line analysis (DigiData 1440A and pClamp10, Molecular Devices). Beginning the day of the treatment, these mice will be subjected to Phase II video-EEG recordings (three 2 hr sessions over 3 days) under the same settings as in Phase I. **Outcomes**: These studies will focus on examining change in EEGs of the mice and the biofield therapists and possible coherence between the mouse and the human brain. **Human EEG data collection**: Human EEG measurements of biofield therapists and the control participants will be conducted at specific time points in all experiments. EEG recordings will be obtained continuously using 64 electrodes in the standard international system placement 10-20 system. Data will be collected in the eyes-open condition, recorded digitally at 1 kHz. Impedances will be brought below threshold recommended by the manufacturer. EEG spectrum will be calculated from the first 5 minutes of artifact-free data at each time point of interest with fast Fourier transform using 4-second epochs with 1/2 seconds of overlapping window advancement. Mean amplitude, power, relative, and absolute values will be computed offline for each of the frequency bands delta (1.5-3.5 Hz), theta (4-7.5), alpha (8-12.0), beta1 (13-16), beta2 (13-32), and gamma (35-45Hertz, Hz). For EEG data collection, we will the ActiCHamp 64 channel system. ActiCHamp 64 is a research grade system that is hardwired and gel-based system. We will seek to determine patterns of resting state EEG oscillatory activity and functional connectivity in the brain of a human known for biophotonic energy transfer by exploring lagged phase synchronization, a nonlinear connectivity measure. Functional images of spectral density will be computed for 6 frequency bands. For functional connectivity analysis we will use a whole brain Brodmann areas approach. Lagged phase synchronization measures the similarity between signals in a particular frequency band based on normalized Fourier transforms thus it is related to nonlinear functional connectivity. **Synchronization of EEG data streams**: We will synchronize EEG of the mouse and human participants using 5-V TTL pulses. The Human EEG brainamp data collection computer will send a TTL pulse to the human EEG amplifier. This amplifier has a mirror capability so the TTL pulse can be forwarded to another device, in this case the mouse EEG amplifier (which also has a TTL input). Custom cabling might have to be developed depending on the shape of the connector each amplifier accepts. **Hypotheses/Questions**: Our research is exploratory in nature, we have research questions to explore as noted below, but no preset hypotheses for the associations as this is such novel research. As the animals will only be exposed to 3 biofield treatments and the total experiment will only last one week, it is unlikely that we will detect changes in tumor size. However, we will examine group differences and report them descriptively. The questions we will seek to explore are: Hypothesis set 1: treatment and mouse EEG • Does the EEG of the mouse change during biofield therapy versus sham therapy? • Is there a difference at baseline between the EEG of a mouse with and without cancer? • Are there differences in the EEG of the mice with and without cancer during biofield therapy? Hypothesis set 2: treatment and human EEG • Does the EEG of the biofield therapist change during treatment? • Are there any time effects for the changes in human EEGs when different groups of mice (cancer vs control) are treated? Hypothesis set 3: mouse EEG and human EEG • Are the mouse and therapist time-synchronized EEGs correlated during biofield therapy in real- time? • Does this synchronization depend on the groups of mice (cancer vs control)? **Data analysis and biostatistical consideration**: First, we will conduct a cortical analysis. For that analysis we will: A) Create individual analysis to determine what activity each participant (therapists and controls) brain is doing both at rest and during task (treatment) and B) Compare each participant (therapists and controls) to a database that will determine regions of interest and function within those regions that are different. Within that data, we will look at connectivity between regions. Second, we will conduct LORETA analysis. Although EEG activity arises from cortical signals, LORETA is a software program that utilizes the inverse solution to infer activity from subcortical structures containing pyramidal cells. This will help determine which brain areas are active in the therapist and control. To explore changes in cortical activity, we will include pre and post comparisons of the EEG. Baseline analyses will include global differences in cortical activation as well as site-specific dominant frequencies for the therapist and control. We will observe baseline differences in amplitude, power, and relative power for delta (0.5-3.5HZ), theta (4-7HZ), alpha (8-12HZ), low beta (13-15 HZ), high beta (16-29HZ), gamma (30+). We will then compare the therapist to each control. We will compute 171 channel combinations of coherence (variability of time differences between two time series in a frequency band), phase differences, correlation (comodulation), cross-spectra, and co-spectra. We will also analyze instantaneous connectivity and lagged connectivity, and quantify components of phase reset (shift in phase followed by phase stability to a new state), and phase locking (Thatcher, 2014). For correlations between mouse and human brain activity, we will use a block design approach to EEGs recorded both in human and mouse during treatment. We will compute coherence measures (correlation in the frequency domain) across all pairs of electrodes of the human and mouse participants, assess statistics, and correct for multiple comparisons using cluster correction methods (similar to Bonferroni correction but adapted for brain imaging data). Although the study is underpowered with the current sample size, the intention is to replicate the whole study with a second biofield therapist and combine the data for a sample size of 16 per group. After accounting for potential missing data, a sample size of at least 14 (mice) in each group will have 80% power to detect a difference in mean of 1.10 standard deviation units (of the EEG outcome) using a two-sample t-test with a 0.05 two-sided significance level. [1]: https://mfr.osf.io/export?url=https://osf.io/download/nh7y2/?direct=&mode=render&format=2400x2400.jpeg