Background Transitory transcendent states share several

1. Transforming transcendence into trait - An electrophysiological approach:The Model of Mindfulness MeditationAviva Berkovich Ohana1, Dr Avi Goldstein1,2, Prof. Joseph Glicksohn1,3 The Leslie and Susan Gonda (Goldschmied) Bar-Ilan University, ISRAEL:1 The Leslie and Susan Gonda (Goldschmied) Multidisciplinary Brain Research Center; 2 Psychology Department; 3 Criminology Department Multi Disciplinary Research Center Laboratory Procedure Interview Questionnaires Alternate Uses Figure 1 We ran a five-way analysis of variance (ANOVA), having one Grouping factor (control, Intermediate, advanced (n=9;8;8, respectively)), and with repeated measures on 4 within- participant factors: Baseline (1, 2), Band (theta: 4-8 Hz, alpha: 8- 13 Hz), Montage (frontal, central, temporal, parietal), and Hemisphere (L, R). The 5-way interaction was found to be significant, F(6, 66) = 3.023, p < .05, adopting the Greenhouse-Geisser p-value. Reflects creativity Background Transitory transcendent states share several Common characteristics, including: higher unity Perception with lower self boundaries, highly positive affect, heightened attention and lower automatization, alterations in temporal and spatial cognition and transition to an exceedingly creative and non-verbal thinking style. These characteristics might become permanent as a result of long training in transcendence–inducing techniques, such as meditation. Research Questions ∆ What are the cortical function changes which are induced by the state of Mindfulness Meditation? ∆ Are these state changes converted with practice, and how (linearly or threshold type), into trait? ∆ What are the cortical function state and trait differences between long–term meditators of Mindfulness vs. Concentrative Meditation? Participants Electrophysiological Methods EEG recordings: 64-channel geodesic net (EGI); 500 Hz Fs; offline 3-100 Hz bandpass filter and 50 Hz notch; referenced to average reference; artifacts manually excluded. Preliminary EEG analyses: 60 epochs (1024 ms) from baseline 1 and 2 were analyzed for power spectral distribution by Multi - Taper analysis (custom written Matlab software), log-transformed, and averaged. 2.5m Eyes Open, 2.5m Eyes Closed Time1 – reflects trait Time 2- reflects state Reflects temporal cognition changes Eyes Closed Time Estimation (X8 trials) S1 (*)/ 1.5s interval /S2 (tone)/ press to terminate tone (x31) Higher amplitude reflects higher attention Lower amplitude reflects lower automatization S1 (number<100)/ 1.5s interval/ S2 (number<100)/ press to choose higher number Embedded Figure Task (x8 trials) Reflects field dependence & spatial cognition changes before meditation After meditation Alternate Uses Oral Report Questionnaires Beginners (<1000 h), n=20 Mindfulness (Vipassana) Meditators Intermediates (<3000 h), n=30 Advanced (>3000 h), n=25 Concentrative (TM) Meditators Preliminary Baseline Results Advanced (>3000 h), n=20 Figure 2 Log alpha power distribution over the scalp in baseline 1 and 2 for one control participant and one advanced meditator ( 36y and 32y old males, respectively). The plot shows EEG for n=1 rather than averages, in order to maintain physiological meaning. Control ∆We found higher frontal alpha and theta power for both meditation groups vs. control, as expected (Fig. 1 and 2). ∆ Higher alpha and theta power (over the whole selected montage) was found for control group after relaxation, but no significant difference between before meditation (trait) and after (state) for both meditation groups (Fig. 1). ∆ Interestingly, there was not any significant difference in slow EEG rhythms between the intermediate meditators and advanced (34±10.6y; 2011±825h accumulating experience, and 41±11.3y; 5700±1700h, respectively). Matched gender/age, n=30

2. Transforming transcendence into trait – An electrophysiological approach:The Model of Mindfulness MeditationAviva Berkovich Ohana The Multidisciplinary Brain Research Center, Bar-Ilan University, ISRAEL