Y P O C T O N O D E S Basic Principles of tRNS: Theory and - - PowerPoint PPT Presentation

Basic Principles of tRNS: Theory and Application

Roi Cohen Kadosh

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Declaration of competing interests

• Scientific Advisory Board, Neuroelectrics Inc.
• Scientific Advisory Board, InnoSphere Inc.
• Consultancy, InnoSphere Inc.

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Noise

If everything else is ideal, then noise is the enemy

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Noise

Can we consider our brain as an ideal system?

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Noise

Benefits have been reported in diverse systems, including:

• Climate models
• Electronic circuits
• Differential equations
• Lasers
• Neural models
• Physiological neural populations and networks
• Chemical reactions
• Ion channels
• SQUIDs (superconducting quantum interference devices)
• Ecological models
• Cell biology
• Financial models
• Psychophysics
• Nanomechanical oscillators
• Organic semiconductor chemistry
• Social systems

McDonnell & Abbott, 2009, PLoS Comp Biol

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Noise

Nonlinearity: presence of noise in a nonlinear system is better for output signal quality than its absence. Noise cannot be beneficial in a linear system

Performance (noise + nonlinearity) > Performance (nonlinearity)

Stochastic facilitation: Random noise enhances the detection of weak stimuli and/or the information content of a signal

(Moss et al., 2004, Clin Neurophysiol; McDonnell & Ward, 2011, Nat Rev Neurosci)

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Noise

Nonlinearity: presence of noise in a nonlinear system is better for output signal quality than its absence. Noise cannot be beneficial in a linear system

Performance (noise + nonlinearity) > Performance (nonlinearity)

McDonnell & Abbott, 2009, PLoS Comp Biol

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Random Noise Stimulation

2005, Ann Neurol

• Used noisy galvanic vestibular stimulation (GVS) to influence

neuronal circuits including the basal ganglia and the limbic system

• 19 Patients with multi system atrophy and/or Parkinson’s disease.
• Noisy GVS boosted the neurodegenerative brains of patients,

including those unresponsive to standard levodopa therapy

• It is also effective in improving autonomic and motor responsiveness

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Transcranial Random Noise Stimulation (tRNS)

Alternating current at random frequencies (Terney et al.,

2008, J Neurosci)

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Terney et al., 2008, J Neurosci

10 min tRNS on MEP

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Advantages over tDCS

• Polarity-independent
• Less sensitive to cortex folding
• Compared to tDCS, it is more comfortable,

which make it potentially advantageous for setting and blinding studies (Ambrus et al., 2010;

Moliadze et al., 2010)

• The 50% perception threshold for both tDCS

conditions was at 0.4mA while this threshold was at 1.2mA in the case of tRNS.

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Chaieb et al., 2015, Front Neurosci

The effect of carbamazepine (CBZ): A sodium channel blocker

A more pronounced effect of voltage-gated sodium channels on tRNS aftereffects

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Perceptual learning

Fertonani et al. 2011, J Neurosci

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Perceptual task

Van der Groen and Wenderoth 2016, J Neurosci

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Snowball et al., 2013, Curr Biol

tRNS over the dlPFC improves cognitive training

D ay M e a n C a lc u la tio n R T s (m s)

1000 2000 3000 4000 5000

tR N S S ham

D ay M e a n D rill R T s (m s )

400 600 800 1000

S ham tR N S

Calculation Training Drill Training

1 2 3 4 5 1 2 3 4 5

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Near Infrared Spectroscopy (NIRS)

An optical imaging technique used to observe: ▪ HbO2 (oxygenated haemoglobin) ▪ HHb (deoxygenated haemoglobin) ▪ HbT (total haemoglobin)

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4 5 6 7 8 P e a k t i m e ( s e c

• n

d s ) H b O

2

S h a m t R N S H H b H b T

F(1, 20)=6.67, p=.018

tRNS improves brain efficiency

Faster Slower

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Snowball et al., 2013, Curr Biol

Long-lasting effect

M e d i a n R T ( m s ) 2 5 3 3 5 4 4 5 5 5 5 6 O l d P r o b l e m s N e w P r o b l e m s S h a m t R N S

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No lasting improvement for drill

M e d i a n R T ( m s )

1 5 2 2 5 3 3 5 4 4 5

S h a m t R N S

p=0.78

Faster Slower

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C a l c u l a t i o n D r i l l 4 6 8 1 1 2 P e a k t i m e ( s e c

• n

d s ) S h a m t R N S

F(1,10)=.49, p=.5 F(1,10)=11.58, p=.007

Long-lasting effect at the physiological level

Faster Slower

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Results

Cappelletti et al. 2013, J Neurosci

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Atypical development

tRNS cap

Looi et al., 2017, Sci Rep

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S e s s io n L e v e l c o m p le te d 1 2 3 4 5 6 7 8 9 6 8 1 0 1 2 1 4 1 6 1 8 2 0 S h a m

tR N S

tRNS affects the learning slopes

F(1,10)=5.9, p<.01 Better Snowball et al., 2013, Curr Biol; Cappelletti et al., 2013, J Neurosci; Popescu et al., 2016, Neuropsychologia; Fertonani et al., 2011, J Neurosci; Terney et al., 2008, J Neurosci

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Online effect: temporary fluctuations in behaviour

• r knowledge that can be observed and measured

during the acquisition process

The Subcomponents of Cognitive Training

Soderstrom & Bjork (2015, Perspect Psychol Sci)

Performance

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Offline effect: relatively permanent changes in behaviour/knowledge

The Subcomponents of Cognitive Training

Soderstrom & Bjork (2015, Perspect Psychol Sci)

Learning

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Experiment 1

d l P F C P P C

• 1
• 5

5 1 O n l i n e ( B )

n=72 Better

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Experiment 1

d l P F C P P C

• 1
• 5

5 1 O f f l i n e ( B )

*

n=72 Better

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Experiment 2

d l P F C P P C

• 1
• 5

5 1 O n l i n e ( B )

n=51 Better

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Experiment 2

d l P F C P P C

• 1
• 5

5 1 O f f l i n e ( B )

* * *

n=51 Better

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The effect is offline-related!

d l P F C P P C

• 1
• 5

5 1 O f f l i n e ( B )

*

d l P F C P P C

• 1
• 5

5 1 O n l i n e ( B ) d l P F C P P C

• 1
• 5

5 1 O n l i n e ( B ) d l P F C P P C

• 1
• 5

5 1 O f f l i n e ( B )

* * *

• Exp. 1
• Exp. 2

n=123, Stimulation x Area: p=.00004, dlPFC: p=0.0008, PPC: p=0.01 Better

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Dose effect and potential mediators

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We aimed to target the top-down cortical attention system; a predominantly right lateralised frontoparietal network

The neural basis of sustained attention

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Sustained attention

1270 1280 1290 1300 1310 1320 1330 1340

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5

65 35 000

• 400
• 400
• 800
• 1200
• 1600
• 2000
• 2400
• Target

Onset

• Contrast
• f

the s mulus begins to decrease from the baseline level (65%)

• Max.

decrease Contrast

• f

the s mulus reaches the lowest

• level

(35%)

• Return

to Baseline Contrast

• f

the s mulus returns to the baseline level (65%)

• Time

(ms) Contrast

• f

S mulus (%)

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Experiment design

 Within-subjects design (n=72)  Each subject received 3 different stimulation

conditions over 3 consecutive days

 Order of stimulation fully randomized  tRNS electrodes placed over F4 and P4 to

target right DLPFC and right IPL

Harty & Cohen Kadosh (Submitted)

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F(4,284) = 3.08, p = .017

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Variability in the response 1mA tRNS

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1mA tRNS reduced TBR

F(2,136) = 5.93, p = .003

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The benefit from tRNS depended on TBR

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Short Quiz

What are the advantages of tRNS over tDCS?

• Polarity-independent
• Less sensitive to cortex folding
• It is more comfortable, which make it potentially

advantageous for setting and blinding studies

• The 50% perception threshold for both tDCS

conditions was at 0.4mA while this threshold was at 1.2mA in the case of tRNS.

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Short Quiz

tRNS seems to interact with: 1) The GABAergic system 2) The dopaminergic system 3) The glutamatergic system 4) 1 and 3 are correct 5) All the answers are correct

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Short Quiz

Based on the material covered here, who would you think be most likely to benefit from tRNS 1) The average person 2) Those who are cognitive below the average 3) Those who are cognitively above the average 4) 1 and 2 5) 1 and 3 as the effect is non-linear

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Short Quiz

The effect of tRNS is: 1) Online-base 2) Offline-base 3) Can be both 4) Neither (you should use tDCS)

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What have we learned

• Noise can be beneficial in nonlinear systems
• Applying noise to the brain improves performance
• The effect can be long-lasting
• tRNS has some advantages over tDCS
• tRNS interacts with voltage-gated sodium channels,

the gultamateric system, and hemodynamic response.

• The effect can be moderated by neurophysiological

trait

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Thanks to

Current and previous lab members Dr Siobhan Harty Dr Beatrix Krause Dr Chung Yen Looi Thomas Page Dr Tudor Popescu Gal Raz Albert Snowball Dr Devin Terhune Olivia Towse Dr George Zacharopoulos Collaborators Dr Jessamy Almquist (Honeywell) Dr Mihaela Duta (Oxford) Prof Margarete Delazer (Innsbruck)

• Prof. Glyn Humphreys (Oxford)

Jenny Lim (Fairely House School) Dr Simon Lolliot (McGill) Dr Ilias Tachtsidis (UCL) Dr Laura Zamarian (Innsbruck) Dr Tingting Zhu (UCL) Prof Glyn Humphreys (Oxford) Staff, parents and children, Fairely House School

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Thank you for your attention

E-mail: roi.cohenkadosh@psy.ox.ac.uk Web: www.psy.ox.ac.uk/research/cohen-kadosh-laboratory

“It is not science fiction, it is already real and it will be a critical part of our future.”

Stephen Hawking, discussing our research TEDx Talk: https://www.youtube.com/watch?v=QoHZ5b-aaX4

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