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Outsmarting Smart Technology to Reclaim our Health and Focus

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8–8.30. Outsmarting Smart Technology to Reclaim our Health and Focus

Dr. Margaret Morris, clinical psychologist, author of Left to Our Own Devices and former senior researcher at Intel

8.45–10.15am. Navigating the Brain Research Landscape

Dr. Peter Whitehouse, Professor of Neurology at Case Western Reserve University
Dr. Nir Grossman, Lecturer in the Division of Brain Sciences at Imperial College London
Dr. Reza Zomorrodi, Project Scientist at University of Toronto’s Centre for Addiction and Mental Health (CAMH)
Chaired by: Rebecca Canter, Associate at the Dementia Discovery Fund (DDF)

Slidedeck supporting presentation and discussion during the 2019 SharpBrains Virtual Summit: The Future of Brain Health (March 7-9th). Learn more at:
https://sharpbrains.com/summit-2019/

Published in: Technology
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Outsmarting Smart Technology to Reclaim our Health and Focus

  1. 1. Outsmarting Smart Technology to Reclaim our Health and Focus Dr. Margaret Morris, clinical psychologist, author of Left to Our Own Devices and former senior researcher at Intel
  2. 2. Left to Our Own Devices Margaret Morris SharpBrains May 8, 2019 @margiemem
  3. 3. The benefits of technologies depend on how much we make them our own.
  4. 4. Stories from Left to Our Own Devices “I’m your Stress Phone!” The Big Mouth Scale Do you Know Where your Children Are? Remembering the Context, Calm Alarms Words with Socially Anxious Friends
  5. 5. Morris et al. Mobile therapy and mood sampling. JMIR 2010 “We looked at his build up . . . towards me and towards the teacher . . . We talked about what happened for me, which was that I was embarrassed.” “I’m your stress phone!”
  6. 6. “I got this email from my scale . . . `Congratulations you’ve lost 15 lbs!’” The Big Mouth Scale
  7. 7. East London Walking home from the bus At the bus stationCentral London “This replaced a daily reminder that our parents have made clear they require from us…” Morris, Carmean, Minyaylov, Ceze. Augmenting Interpersonal Communication through Connected Lighting. CHI 2017 Do You Know Where Your Children Are?
  8. 8. “Now what could that be?” Remembering the Context
  9. 9. Calm Alarms
  10. 10. Words with Socially Anxious Friends
  11. 11. There’s not one app for that. Broaden the idea of health technology to include the tools that are already part of patient’s lives. No size fits all. Encourage and design for adaptation. Cognitive health is social. Support interpersonal uses of personal health technologies.
  12. 12. Margie.mem@gmail.com @margiemem
  13. 13. Navigating the Brain Research Landscape Chaired by: Rebecca Canter, Associate at the Dementia Discovery Fund (DDF) Dr. Nir Grossman, Lecturer in the Division of Brain Sciences at Imperial College London Dr. Reza Zomorrodi, Project Scientist at University of Toronto’s Centre for Addiction and Mental Health (CAMH) Dr. Peter Whitehouse, Professor of Neurology at Case Western Reserve University
  14. 14. Wising up: designing a course* for the future Peter J Whitehouse MD-PhD Case Western Reserve University, University of Toronto, and University of Oxford Intergenerational Schools International * A real and virtual; intergenerative and transdisciplinary brain health course
  15. 15. The Course • Learning objectives - rehumanized humans, flourishing civilizations, and resilient ecosystems • Learning subjectives –brain health with depth (purpose) and breadth (community) • The Future – Brain health in an unhealthy society –Alzheimers explained the books • Intergenerative – Intergenerational Schools, • Transdisciplinarity – e.g. a deeper bioethics • Trees and Forests –Sylvanus the video game • Wising up about wisdom – the course
  16. 16. New Concepts of Alzheimer's Disease
  17. 17. Brain Health in an Unhealthy Society: a social prescription for wising up together Danny George PhD and Peter Whitehouse MD-PhD Hopkins Press, forthcoming neoliberalism (individual-focused, market fundamentalism) causes dementia through social determinants of health
  18. 18. WELCOME TO OUR COMMUNITY OF LIFELONG LEARNERS AND SPIRITED CITIZENS!
  19. 19. The Intergenerational Model Recognitions and Partnerships
  20. 20. Kay Fuller Freeway Fighter 1960’s environmental activist
  21. 21. Eco Wise and friends Digital Story Telling VR and Transmedia
  22. 22. Modelling nature (the school garden) in VR
  23. 23. Saving the Forest Video Game intergenerational systems thinking
  24. 24. Goal – to save the healing spirit of the forest
  25. 25. Wising up: designing a course* for the future Content – brain health, positive aging, intergenerational learning, sustainable communities, digital technologies, art Process – participatory design, experiential, service Students – elders, undergrads, youngsters Capstone – spirited citizenship in a museum
  26. 26. Iain McGilchrist –attention and the divided brain
  27. 27. Noninvasive deep brain stimulation via delivery of temporally interfering electric fields Nir Grossman, PhD Nir Grossman, et al., Cell 169.6 (2017) Nir Grossman, Science 361.6401 (2018) Division of Brain Sciences, Centre for Bioinspired Technology, Centre for Neurotechnology, Imperial College London (ICL) UK Dementia Research Institute (DRI) Media Lab and McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT)
  28. 28. 1 billion people depression obesity anxiety chronic pain sleep disorders addiction Alzheimer’s disease stroke TBI vision losshearing loss migraine epilepsy Parkinson’s disease ADHD multiple sclerosis schizophrenia autism ALS tinnitus Brain Stimulation Invasive, Focal, Deep Non-invasive, Dispersed, Superficial Invasive, Focal, Deep Non-invasive, Dispersed, Superficial Non-invasive, Steerable, 3D Focal Brain Stimulation Without tissue manipulation (chemical, genetic etc.)
  29. 29. Outline Principle of Stimulation Validation 1: Driving Neural Activity in Vivo, Assessment With Single Cell Recording Validation 2: Recruitment of Deep Brain Structures, Without Overlying Layers Validation 3: Safety Validation 4: Functional Mapping of Brain Regions, With Steerable Stimulation Validation 5: Steerable activation of resting-state BOLD fMRI in humans (unpublished)
  30. 30. Principle of stimulation Temporal Interference 𝐼2(𝑓1 + ∆𝑓)𝐼1(𝑓1) 𝑥 𝑦 𝐸1 𝑥, 𝑦 𝐸2 𝑥, 𝑦 𝐸2 𝑦 𝑓1, 𝑡 𝐸1 𝑦 𝑓1, 𝑡 𝐸2 𝐸1 𝐸𝑛𝑣𝑒𝑙𝑜𝑝𝑒(∆𝑓, 𝑡) 𝐸2 𝑦 𝑓1, 𝑡 𝐸𝑛𝑣𝑒𝑙𝑜𝑝𝑒(∆𝑓, 𝑡) 𝐸2 𝐸1 𝑦 𝑓1, 𝑡 + 𝐸2 𝑦 𝑡𝐸1 𝑦 𝑡 𝐸1
  31. 31. Validation 1: Driving Neural Activity in-vivo 20 mV 200 𝜇A 100 ms 2 kHz 5sweeps -50 mV 𝐼1 𝐼2 Patch-clamp electrode Single cell activity 20 mV 200 𝜇A 100 ms 10 Hz 5sweeps -50 mV 5 ms 20 mV 100 𝜇A 100 ms -50 mV 100 𝜇A 5 ms 5sweeps 2.01 kHz 2 kHz All traces were filtered (Butterworth high-pass 100Hz and band-stop 1kHz-15kHz) to remove stimulation artefacts.
  32. 32. 100 𝜇A 2.01 kHz 100 ms 20 mV 100 𝜇A 2 kHz 5 ms 10 mV -47 mV 2 kHz 200 𝜇A 100 ms 20 mV -48 mV -54 mV 100 ms 20 mV 2.01 kHz 2 kHz 400 𝜇A 400 𝜇A 10 ms 10 mV -49 mV 100 ms 20 mV 2 kHz 2 kHz 400 𝜇A 400 𝜇A Validation 1: Driving Neural Activity in-vivo 0.25 0.5 Fractionofcells 0.25 s ramp-up (Crtx) 0.5 s ramp-up (Hipp) Crtx, Cortex; Hipp, Hippocampus; All traces were filtered (Crtx: Butterworth high-pass 100Hz and band-stop 1kHz- 15kHz; Hipp: Butterworth band-stop 1kHz-15kHz) to remove stimulation artefacts. Fraction of cells that spiked during high-frequency ramp-ups (pooled are 1 kHz and 2 kHz trials)
  33. 33. Validation 1: Driving Neural Activity in-vivo One-way ANOVA with Bonferroni post-hoc test. ∗∗ P < 0.00001 for comparison vs. mean spontaneous firing rate. Cortex (Crtx):10 Hz stimulation (200 μA, n = 7 cells from 4 mice), TI stimulation with 1 kHz + 1.01 kHz (current sum 200 μA, n = 6 cells from 2 mice), TI stimulation with 2 kHz + 2.01 kHz (current sum 200 μA, n = 7 cells from 3 mice), 1 kHz stimulation (200 μA, n = 5 cells from 2 mice), 2 kHz stimulation (200 μA, n = 6 cells from 3 mice). Hippocampus (Hipp):10 Hz (current sum 714 ± 367 μA mean ± SD, n = 6 cells from 3 mice), TI stimulation with 2 kHz + 2.01 kHz (current sum 733 ± 100 μA, n = 8 cells from 4 mice), stimulation with two sinusoids at 2 kHz (current sum 880 ± 178 μA, n = 5 cells from 3 mice). Mean (± st.d.) Firing Rate (Crtx) 𝐼1 𝐼2 Patch-clamp electrode *** *** *** n.s. n.s. n.s. spontaneous firing *** *** n.s. spontaneous firing Mean (± st.d.) Firing/Bursting Rate (Hipp)
  34. 34. 𝐸𝐴𝑀 𝑥 Simulation 0 3.8 V/m 𝐸𝐴𝑀 𝑦 Measurement Physics Characterization Effect of Electrode Geometry 𝐸𝐴𝑀 𝑥 𝐸𝐴𝑀 𝑦 0 0.9 V/m 𝐸𝐴𝑀 𝑥 𝐸𝐴𝑀 𝑦 0 0.3 V/m Phantom: non- conductive cylinder, 50 mm diameter, 10 mm height, filled with saline solution; Electrodes: 1 mm diameter silver wires; Currents: 𝐼1 = 𝐼2 = 1 𝑚𝐴.
  35. 35. Overlying cortex Hippocampus Neural activity level via c-Fos expression Validation 2: Recruitment of deep brain structures
  36. 36. Validation 2: Recruitment of deep brain structures 𝟐𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟐. 𝟎𝟏𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟐𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟐𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟏𝟎𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟏𝟎𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 500 µ𝑚 Kindling stimulation: see for example Dragunow, M., Nature, 1987
  37. 37. 𝟏𝟎𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟏𝟎𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 Validation 2: Recruitment of deep brain structures 𝟐𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟐𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟐𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 𝟐. 𝟎𝟏𝐤𝐇𝐳, 𝟎. 𝟏𝟓𝐦𝐀 500 µ𝑚 n.s. ** n.s. 𝐶𝑟𝑡𝑥 𝑈𝐸 − 𝐶𝑟𝑡𝑥 𝐵𝐸 + 𝐶𝑟𝑡𝑥 𝐵𝐸 − 𝐻𝑖𝑝𝑝+ 𝐻𝑖𝑝𝑝− 𝐶𝑟𝑡𝑥 𝑈𝐸 + n = 4 mice ** p < 0.00001 𝐶𝑟𝑡𝑥 𝑈𝐸 + 𝐶𝑟𝑡𝑥 𝑈𝐸 − 𝐻𝑖𝑝𝑝+ 𝐻𝑖𝑝𝑝+ n.s. n.s. n = 4 mice 𝐶𝑟𝑡𝑥 𝑈𝐸 + 𝐶𝑟𝑡𝑥 𝑈𝐸 − 𝐻𝑖𝑝𝑝+ 𝐻𝑖𝑝𝑝− * ** n = 3 mice * p < 0.05 ** p < 0.00001 Mean (± st.d.) % c-Fos cells Significance was confirmed with one-way ANOVA with Bonferroni post-hoc test. 𝐶𝑟𝑡𝑥 𝑈𝐸 −𝐶𝑟𝑡𝑥 𝐵𝐸 + 𝐶𝑟𝑡𝑥 𝐵𝐸 − 𝐻𝑖𝑝𝑝− 𝐶𝑟𝑡𝑥 𝑈𝐸 + 𝐻𝑖𝑝𝑝+ 𝐶𝑟𝑡𝑥 𝑈𝐸 − 𝐻𝑖𝑝𝑝− 𝐶𝑟𝑡𝑥 𝑈𝐸 + 𝐻𝑖𝑝𝑝+ 𝐶𝑟𝑡𝑥 𝑈𝐸 − 𝐻𝑖𝑝𝑝− 𝐶𝑟𝑡𝑥 𝑈𝐸 + 𝐻𝑖𝑝𝑝+
  38. 38. Overlying cortex Hippocampus Safety • cell death (caspase-3) • neuron count (NeuN) • astrocyte state (GFAP) • microglial cell state (Iba1) • synaptic integrity (Syp) • DNA damage (γH2AX ) Validation 3: Safety Awake animal
  39. 39. Validation 3: Safety
  40. 40. SimulationMeasurement Physics Characterization Effect of Current Ratios 𝐸𝐴𝑀 𝑥 𝐸𝐴𝑀 𝑦 0 0.3 V/m 𝑰 𝟏: 𝑰 𝟐 = 𝟏: 𝟏 𝑰 𝟏 + 𝑰 𝟐 = 𝟐 𝒎𝑨 𝐸𝐴𝑀 𝑥 𝐸𝐴𝑀 𝑦 0 0.2 V/m 𝑰 𝟏: 𝑰 𝟐 = 𝟏: 𝟐. 𝟓 𝑰 𝟏 + 𝑰 𝟐 = 𝟐 𝒎𝑨 𝐸𝐴𝑀 𝑥 𝐸𝐴𝑀 𝑦 0 0.2 V/m 𝑰 𝟏: 𝑰 𝟐 = 𝟏: 𝟒 𝑰 𝟏 + 𝑰 𝟐 = 𝟐 𝒎𝑨 Phantom: non- conductive cylinder, 50 mm diameter, 10 mm height, filled with saline solution; Electrodes: 1 mm diameter silver wires.
  41. 41. Whisker Whisker Forelimb 𝐼1 𝐼2 Validation 4: Functional Mapping of Brain Regions Forelimb N = 9 mice Significance of movements was confirmed with unpaired t-test with Bonferroni correction; * p < 0.002, ** p < 0.00001 Significance between current ratios was confirmed with one-way ANOVA with Bonferroni post-hoc test; * p < 0.05 Mice motor cortex map: see for example K.A. Tennant, Cerebral Cortex, 2011 Mean (± s.e.m) Whisker Movement Mean (± s.e.m) Forepaw Movement Movement Contralateral (to 𝐼1) Ipsilateral (to 𝐼1)
  42. 42. Noresponse ** * * * Mean (± st.d.) Threshold vs. Difference Frequency Mean (± st.d.) Threshold vs. Carrier Frequency
  43. 43. Duke ViP3.0 modelSubject 07 Study design × 4 (random order) 10 Hz No stimulation 2 kHz No stimulation TI (1:1) No stimulation TI (3:1) No stimulation TI (1:3) No stimulation Sham No stimulation Electrode configuration Validation 5: Resting-state fMRI in human 𝐸 < 1 𝑒 𝐸 𝑚𝑎𝑥ROI frontal ROI dorsal Personalized FEM modelStructure MRI E Field & ROI Analysis strategy Location: Martinos Imaging Center, MIT; Subjects: 9 healthy volunteers (3 females; mean age 27.5 ±5); MRI: 3T MAGNETOM Trio (Siemens Healthcare); Unpublished data
  44. 44. TI (1:1) 2.01 kHz, 1 mA + 2 kHz, 1 mA TI (3:1) 2.01 kHz, 1.5 mA + 2 kHz, 0.5 mA Unpublished data Validation 5: Resting-state fMRI in human First-level analysis: small volume correction with cluster threshold at p<0.01. Second-level analysis: individual T-maps were thresholded at p<0.01 (voxel-level) and peak clusters were extracted, significance between frontal and dorsal ROIs was characterized via pairwise t-test, * p < 0.05, n = 9 subjects TI (1:3) 2.01 kHz, 0.5 mA, 2 kHz, 1.5 mA Peak T-score (Condition-Baseline; across subjects)
  45. 45. Summary Temporal Interference (TI) – a new brain stimulation modality • Based on superposition of multiple kHz electric fields Validation experiments • Driving action potential activity in-vivo (mouse) • Recruitment of hippocampus without overlying cortex (mouse) • Functional mapping of motor cortex with fixed electrodes (mouse) • Safety (mouse) • Steerable activation of resting-state BOLD fMRI (humans) New capabilities to existing brain stimulation ecosystem No tissue manipulation → Fast clinical adoption
  46. 46. David Bono (MIT) Suhasa Kodandaramaiah (MIT, University of Minnesota) Andrii Rudenko (MIT, City University of York) Nina Dedic (MIT) Ho-Jun Suk (MIT) Antonino Cassara (IT’IS foundation) Esra Neufeld (IT’IS foundation) Sheeba A. Anteraper (MIT) Atsushi Takahashi (MIT) Niels Kuster (IT’IS foundation) Li-Huei Tsai (MIT, Broad Institute) Alvaro Pascual-Leone (Harvard) Ed S. Boyden (MIT) Acknowledgments
  47. 47. Shining light on the Brain: Photobiomodulation as a new non-Invasive Brain Stimulation Reza Zomorrodi, Ph.D Project Scientist Temerty Centre for Therapeutic Brain Intervention Centre for Addiction and Mental Health
  48. 48. The Brain is an Electro-Chemical organ Idan Segev, What changes in the brain when we learn? Franco Israeli Scientific Colloquium on Memory, Paris, October 23 Rd , 2006
  49. 49. Electrochemical impulse Reza Zomorrodi et al ,Modeling thalamocortical cell: impact of Ca2+ channel distribution and cell geometry on firing pattern, Front. Comput. Neurosci., 2008 Neurons communicate with each other via electrical events called 'action potentials' and chemical neurotransmitters.
  50. 50. What does influence neuron firing pattern? Picture from Wikipedia (http://en.wikipedia.org/wiki/Neuron) extracellularoscillations Dendriticmorphology Intercellular, Extracellular properties….whatever a neuron is made of
  51. 51. Does function follow form? $$$ What does influence neuron firing pattern? Intercellular, Extracellular properties….whatever a neuron is made of Alter any of these properties  change in firing pattern Brain Stimulation
  52. 52. Polanaia R, et al. Studying and Modifying Brain Functions with Non-invasive Brain Stimulation. Nat. Neurosc. 2018. Existing “Non-Invasive” Brain Stimulation Methods Based on Electro-Chemical properties of the Brain
  53. 53. Polanaia R, et al. Studying and Modifying Brain Functions with Non-invasive Brain Stimulation. Nat. Neurosc. 2018. Existing “Non-Invasive” Brain Stimulation Methods Based on Photo-Chemical properties of the Brain Photo- Biomodulation
  54. 54. Hamblin MR, Mechanisms and applications of the anti-inflammatory effects of photobiomodulation, AIMS Biophysics, 2017, 4(3): 337 Photobiomodulation(PBM) • Cytochrome c oxidase in respiratory chain absorbs mainly red (and NIR) light by heme and copper; • Heat-gated TRP ion channels absorb NIR (and blue light) via structured water; • Opsins absorb mainly blue/green light via cis-retinal; • Flavoproteins and cryptochromes absorb mainly blue light via pterin The PBM biological response begins with chromophores(photoreceptors), photon accepting molecules which convert light into signals that can stimulate certain biological processes
  55. 55. Photobiomodulation : Visual stimulation (Light Therapy) Circadian Rhythms – Our Body ClockPhotoreceptors Zvi N Roth et al Stimulus vignetting and orientation selectivity in human visual cortex, eLife 2018 orientation selectivity in human visual cortex
  56. 56. Accidental discovery of photobiostimulation in 5 steps! almost 50 years ago by Endre Mester in Hungary Experiments with mice 1. Could laser be used to treat cancerous tumors? 2. Used low power ruby laser (694 nm) 3. Laser treatments did NOT kill tumor cells 4. Laser treatments DID enhance healing of incisions and hair growth 5. First to observe photobiostimulation Photobiomodulation: Endre Mester, M.D. 1903-1984 Skin/injuries; Skin/pathology; Wound Healing/radiation
  57. 57. Karu T.I. (2003). Low-power laser therapy. In: Biomedical Photonics Handbook (T. VoDinh, ed.) CRC Press, Boca Raton, FL, 48, pp. 1-2 Incoming red and NIR photons are absorbed in cell mitochondria, producing reactive oxygen species (ROS) and releasing nitric oxide (NO), which leads to gene transcription via activation of transcription factors (NF- kB and AP1). Photobiomodulation : Transcranial photobiomodulation
  58. 58. Photobiomodulation benefits Hamblin, MR. Shining light on the head: Photobiomodulation for brain disorders. BBA Clin. 2016 Oct 1;6:113-124. eCollection 2016. 1. Increases ATP synthesis 2. Stimulates cell growth 3. Increases cell metabolism 4. Improves cell regeneration 5. Invokes an anti-inflammatory response 6. Promotes edema reduction 7. Reduces fibrous tissue formation 8. Stimulates nerve function 9. Reduces the production of substance P 10.Stimulates long term production of NO 11. Decreases the formation of bradikynin, histamine, and acetylcholine 12. Stimulates production of endorphins
  59. 59. Explanatory levels of brain stimulation Electro/Magneto-chemical Photo-chemical
  60. 60. Typical neuromodulation techniques • Intensity? • Frequency? • Location?(important target : cerebral cortex, hippocampus, brainstem) • Stimulation duration? • Brain state? (task, no-task) (a) transcranial magnetic stimulation (TMS), (b) transcranial direct current stimulation (tDCS), (c) Deep brain stimulation (DBS), and (d) transcranial ultrasound stimulation. (a) (b) (c) (d) tDCS TMS TUS DBS Common tDCS/tACS montages and corresponding simulated electric field distribution.
  61. 61. Photobiomodulation • Intensity? • Frequency? • Location? • Stimulation duration? • Brain state? (task, no-task)
  62. 62. PMB-EEG study “Targeting several location at once” transcranial PBM Default mode network hubs Intranasal PBM Does it really work ?! $$$
  63. 63. Exploring the effect of Photobiomodulation(PMB) on cortical oscillation using EEG
  64. 64. Double-blind study on 20 Healthy control
  65. 65. PBM-EEG study: Power Spectrum analysis Topographical distribution of EEG power spectrum for active/sham and per/post PBM Average (over all channels) of EEG power spectrum for active/sham and per/post PBM Active: Post-Pre Sham: Post-Pre Pre: Active-Sham Post: Active-Sham • Both Active and Sham significantly changed EEG power spectrum • Active tPBM caused a depression in lower frequency (delta, theta) and facilitation in higher frequency bands(alpha, beta, gamma) power spectrum
  66. 66. PBM-EEG study: Connectivity analysis
  67. 67. Connectivity Analysis A network based on weighted phase lag index (wPLI) was constructed and graph measures were employed to evaluate network features. wPLI is a functional connectivity measure and assesses the phase ‘lagging’ consistency between each pair of EEG channels To assess segregation: Cluster Coefficient CC 𝒗 = 𝟐∗(# 𝒍𝒊𝒏𝒌𝒔 𝒃𝒆𝒕𝒘𝒆𝒆𝒏 𝒏𝒆𝒊𝒈𝒉𝒃𝒐𝒓 𝒐𝒇 𝒗) 𝒅𝒆𝒈𝒓𝒆𝒆 𝒐𝒇 𝒗 To assess integration: Characteristic path length (CPL) = 𝒗 𝒋 𝑳𝒗𝒋 𝑵(𝑵−𝟏) To assess the efficiency of information flow: Efficacy (v) = 𝟏 (𝑵−𝟏) 𝒗≠𝒋 𝟏 𝑳𝒗𝒋 Lvj is the shortest path length between v th node and j th node. N is total number of nodes. CC = 1 CC = 1/2 CC = 0 PBM-EEG study: Connectivity analysis
  68. 68. PBM-EEG study: Connectivity analysis Frequency band (Hz) Sparcity level (1-99)% Connectivityindex • Only active tPBM changed connectivity index • Most significant change occurred in Alpha frequency band network
  69. 69. Does “tPBM” really work ?! First Double-Blind pilot study results showed that it: • changed cortical oscillation and • altered Neural network connectivity Photobiomodulation is a new non-invasive brain stimulation, which is easily • Can target several location • Can modulate brain activity • and Safe & Easy to use
  70. 70. Thank You! Reza Zomorrodi, Ph.D Project Scientist Temerty Centre for Therapeutic Brain Intervention Centre for Addiction and Mental Health Unit 4-1, Office 125A 1001 Queen Street West, Toronto, ON. M6J 1H4 Reza.Zomorrodi@camh.ca (Tel) 416-535-8501 ext 30205 I would be happy to hear your questions/comments tPMB-EEG study was supported by Vielight Inc., I no competing or conflict of interest, financial and non-financial.
  71. 71. Access recorded talks, Q&A, and more at: SharpBrains.com

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