Transforming transcendence into trait - An electrophysiological approach:The Model of Mindfulness Meditation

Aviva Berkovich Ohana1, Dr Avi Goldstein1,2, Prof. Joseph Glicksohn1,3

BackgroundTransitory 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 MethodsEEG 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 Matlabsoftware), log-transformed, and averaged.

Bar-Ilan University, ISRAEL: 1 The Leslie and Susan Gonda (Goldschmied) Multidisciplinary Brain Research Center; 2 Psychology Department; 3 Criminology Department

Mindfulness (Vipassana)

Meditators

Beginners (<1000 h), n=20

Concentrative (TM)

Meditators

Control

Intermediates (<3000 h), n=30

Advanced (>3000 h), n=25

Matched gender/age, n=30

Advanced (>3000 h), n=20

Baseline

EEG

Time

Production

Simple

CNV

Choice

CNV

EFT

Meditation/

Relaxation15 min

Laboratory Procedure

2.5m Eyes Open ,

2.5m Eyes Closed

Eyes Closed Time

Estimation (X8 trials)

S1 (*)/ 1.5s interval/S2 (tone)/ press to terminate tone (x31)

S1 (number<100)/ 1.5s interval/S2 (number<100)/ press to choose higher number

Embedded Figure

Task (x8 trials)

Alternate UsesOral Report

Questionnaires

After

meditationbefore meditation

InterviewQuestionnaires

Alternate Uses

0

0.5

1

1.5

2

Control Intermediate Advanced

F3-theta-b1F4-theta-b1C3-theta-b1C4-theta-b1T3-theta-b1T4-theta-b1P3-theta-b1P4-theta-b1

Log

(Pow

er)

Group

Baseline 1

A

0

0.5

1

1.5

2

Control Intermediate Advanced

F3-theta-b2F4-theta-b2C3-theta-b2C4-theta-b2T3-theta-b2T4-theta-b2P3-theta-b2P4-theta-b2

Log

(Pow

er)

Group

Baseline 2

B

0

0.5

1

1.5

2

Control Intermediate Advanced

F3-alpha-b1F4-alpha-b1C3-alpha-b1C4-alpha-b1T3-alpha-b1T4-alpha-b1P3-alpha-b1P4-alpha-b1

Log

(Pow

er)

Group

Baseline 1

C

0

0.5

1

1.5

2

Control Intermediate Advanced

F3-alpha-b2F4-alpha-b2C3-alpha-b2C4-alpha-b2T3-alpha-b2T4-alpha-b2P3-alpha-b2P4-alpha-b2

Log

(Pow

er)

Group

Baseline 2

D

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.

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.

CONTROLbaseline 1

CONTROLbaseline2

ADVANCEDbaseline 1

ADVANCED

baseline 2

-2

-1

0

1

2

Time1 – reflects traitTime 2- reflects state

Reflects temporal

cognition changes

Higher amplitude

reflects higherattention

Lower amplitudereflects lower

automatization

Reflects field

dependence & spatial

cognition changes

Reflects creativity

Preliminary Baseline ResultsFigure 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.

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.

∆ 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).

Transforming transcendence into trait – An electrophysiological approach:The Model of Mindfulness Meditation

Aviva Berkovich OhanaThe Multidisciplinary Brain Research Center, Bar-Ilan University, ISRAEL