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

Feb 21, 2023 •167 likes •648 views

Y P O C T O N O D E S Basic Principles of tRNS: Theory and A Application E L P Roi Cohen Kadosh Y P O Declaration of competing interests C T Scientific Advisory Board, Neuroelectrics Inc. O Scientific Advisory

  1. Y P O C T O N O D E S Basic Principles of tRNS: Theory and A Application E L P Roi Cohen Kadosh

  2. Y P O Declaration of competing interests C T • Scientific Advisory Board, Neuroelectrics Inc. O • Scientific Advisory Board, InnoSphere Inc. N • Consultancy, InnoSphere Inc. O D E S A E L P

  3. Y P O C T O N O D E S A E L P

  4. Y P Noise O C If everything else is ideal, then noise is the enemy T O N O D E S A E L P

  5. Y P Noise O C Can we consider our brain as an ideal system? T O N O D E S A E L P

  6. Y P Noise O Benefits have been reported in diverse systems, including: C • Climate models T • Electronic circuits • Differential equations O • Lasers N • Neural models • Physiological neural populations and networks O • Chemical reactions D • Ion channels • SQUIDs (superconducting quantum interference devices) E • Ecological models S • Cell biology A • Financial models E • Psychophysics L • Nanomechanical oscillators P • Organic semiconductor chemistry • Social systems McDonnell & Abbott, 2009, PLoS Comp Biol

  7. Y P Noise O C Nonlinearity : presence of noise in a nonlinear system is better for output signal quality than its absence. T Noise cannot be beneficial in a linear system O N Performance (noise + nonlinearity) > Performance (nonlinearity) O D Stochastic facilitation : Random E noise enhances the detection of S weak stimuli and/or the A information content of a signal E (Moss et al., 2004, Clin Neurophysiol; McDonnell & Ward, 2011, Nat Rev Neurosci) L P

  8. Y P Noise O C Nonlinearity : presence of noise in a nonlinear system is better for output signal quality than its absence. T Noise cannot be beneficial in a linear system O N Performance (noise + nonlinearity) > Performance (nonlinearity) O D E S A E L P McDonnell & Abbott, 2009, PLoS Comp Biol

  9. Y P O C T O N O D E S A E L P

  10. Y P Random Noise Stimulation O C T O N O D • Used noisy galvanic vestibular stimulation (GVS) to influence E neuronal circuits including the basal ganglia and the limbic system S • 19 Patients with multi system atrophy and/or Parkinson’s disease. A • Noisy GVS boosted the neurodegenerative brains of patients, E including those unresponsive to standard levodopa therapy L P • It is also effective in improving autonomic and motor responsiveness 2005, Ann Neurol

  11. Y P Transcranial Random Noise Stimulation (tRNS) O C Alternating current at random frequencies (Terney et al., 2008, J Neurosci) T O N O D E S A E L P

  12. Y P O 10 min tRNS on MEP C T O N O D E S A E Terney et al., L 2008, J P Neurosci

  13. Y P O Advantages over tDCS C T • Polarity-independent O N • Less sensitive to cortex folding O • Compared to tDCS, it is more comfortable, D which make it potentially advantageous for E setting and blinding studies (Ambrus et al., 2010; S Moliadze et al., 2010) A E • The 50% perception threshold for both tDCS L conditions was at 0.4mA while this threshold P was at 1.2mA in the case of tRNS.

  14. Y P O The effect of carbamazepine (CBZ): C A sodium channel blocker T A more pronounced effect of voltage-gated sodium channels on tRNS O aftereffects N O D E S A E Chaieb et L al., 2015, P Front Neurosci

  15. Y P Perceptual learning O C T O N O D E S A E L P Fertonani et al. 2011, J Neurosci

  16. Y P Perceptual task O C T O N O D E S A E L P Van der Groen and Wenderoth 2016, J Neurosci

  17. Y P O tRNS over the dlPFC improves C cognitive training T O Drill Training Calculation Training N M e a n C a lc u la tio n R T s (m s) 5000 1000 S ham O S ham M e a n D rill R T s (m s ) tR N S tR N S 4000 D 800 3000 E 600 S 2000 400 A 1000 E 1 2 3 4 5 1 2 3 4 5 D ay D ay L P Snowball et al., 2013, Curr Biol

  18. Y P O Near Infrared Spectroscopy (NIRS) C An optical imaging technique used to observe: T ▪ HbO 2 (oxygenated haemoglobin) O ▪ HHb (deoxygenated haemoglobin) N ▪ HbT (total haemoglobin) O D E S A E L P

  19. Y P tRNS improves brain efficiency O C Slower 8 0 T S h a m O t R N S N ) 7 0 s d n O o c e D 6 0 ( s e E t i m S 5 0 k a A e P E Faster 4 0 L H b O H H b H b T 2 P F(1, 20)=6.67, p=.018

  20. Y P O Long-lasting effect C T O 6 0 0 0 S h a m N 5 5 0 0 t R N S ) s O 5 0 0 0 ( m D T 4 5 0 0 R n E 4 0 0 0 i a d S e 3 5 0 0 M A 3 0 0 0 E 2 5 0 0 L P O l d P r o b l e m s N e w P r o b l e m s Snowball et al., 2013, Curr Biol

  21. Y P O No lasting improvement for drill C T O Slower 4 5 0 0 N 4 0 0 0 s ) O ( m 3 5 0 0 D T R 3 0 0 0 n E i a 2 5 0 0 d S e M A 2 0 0 0 E Faster 1 5 0 0 L S h a m t R N S P p=0.78

  22. Y P Long-lasting effect at the physiological O level C T Slower 1 2 0 S h a m O ) s N t R N S d n 1 0 0 o O c e ( s D 8 0 e t i m E k S a 6 0 e A P E Faster L 4 0 C a l c u l a t i o n D r i l l P F(1,10)=11.58, p=.007 F(1,10)=.49, p=.5

  23. Y P Results O C T O N O D E S A E L P Cappelletti et al. 2013, J Neurosci

  24. Y P Atypical development O C T O N O tRNS cap D E S A E L P Looi et al., 2017, Sci Rep

  25. Y P tRNS affects the learning slopes O C 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 T Better 2 0 S h a m tR N S O N 1 8 L e v e l c o m p le te d O 1 6 D 1 4 E 1 2 S 1 0 A E 8 L 6 P F(1,10)=5.9, p<.01 1 2 3 4 5 6 7 8 9 S e s s io n

  26. Y P O The Subcomponents of Cognitive C Training T O Online effect: temporary fluctuations in behaviour or knowledge that can be observed and measured N during the acquisition process O D Performance E S A E L P Soderstrom & Bjork (2015, Perspect Psychol Sci)

  27. Y P O The Subcomponents of Cognitive C Training T O Offline effect: relatively permanent changes in behaviour/knowledge N O D Learning E S A E L P Soderstrom & Bjork (2015, Perspect Psychol Sci)

  28. Y P O Experiment 1 C T O N 1 0 0 0 O 5 0 0 ) ( B D e 0 l i n n O E - 5 0 0 Better S - 1 0 0 0 d l P F C P P C A E L P n=72

  29. Y P O Experiment 1 C T O N 1 0 0 0 O 5 0 0 ) ( B D e 0 f f l i n O E - 5 0 0 Better S * - 1 0 0 0 d l P F C P P C A E L P n=72

  30. Y P O Experiment 2 C T O N 1 0 0 0 O 5 0 0 ) ( B D e 0 l i n n O E - 5 0 0 Better S - 1 0 0 0 d l P F C P P C A E L P n=51

  31. Y P O Experiment 2 C T O N 1 0 0 0 * * O 5 0 0 ) ( B D e 0 f f l i n O E - 5 0 0 Better S * - 1 0 0 0 d l P F C P P C A E L P n=51

  32. Y P O The effect is offline-related! C T 1 0 0 0 1 0 0 0 O 5 0 0 5 0 0 Exp. 1 ) ) ( B ( B e e N 0 0 l i n f f l i n n O O - 5 0 0 - 5 0 0 O * - 1 0 0 0 - 1 0 0 0 d l P F C P P C d l P F C P P C Better D 1 0 0 0 E 1 0 0 0 * * 5 0 0 S 5 0 0 ) ) ( B ( B Exp. 2 e e 0 0 l i n A f f l i n n O O - 5 0 0 E - 5 0 0 - 1 0 0 0 * - 1 0 0 0 L d l P F C P P C d l P F C P P C P n=123, Stimulation x Area: p=.00004, dlPFC: p=0.0008, PPC: p=0.01

  33. Y P O C T O N Dose effect and potential mediators O D E S A E L P

  34. Y P The neural basis of sustained attention O C We aimed to target the top-down cortical attention system; a predominantly right lateralised frontoparietal network T O N O D E S A E L P

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