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/

5. 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

6. Left to Our Own Devices
Margaret Morris
SharpBrains
May 8, 2019
@margiemem

8. The benefits of technologies depend on how much we make them our own.

9. 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

10. 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!”

11. “I got this email from my scale . . . `Congratulations you’ve lost 15 lbs!’”
The Big Mouth Scale

12. 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?

13. “Now what could that be?”
Remembering the Context

14. Calm Alarms

15. Words with Socially Anxious
Friends

16. 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.

17. Margie.mem@gmail.com
@margiemem

19. 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

20. 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

22. 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

23. New Concepts of Alzheimer's Disease

25. 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

26. WELCOME TO OUR
COMMUNITY OF
LIFELONG LEARNERS
AND SPIRITED
CITIZENS!

27. The Intergenerational Model
Recognitions and Partnerships

29. Kay Fuller
Freeway Fighter
1960’s
environmental
activist

32. Eco Wise and friends
Digital Story Telling
VR and Transmedia

33. Modelling nature (the school garden) in VR

34. Saving the Forest Video Game
intergenerational systems thinking

37. Goal – to save the healing spirit of the forest

40. 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

41. Iain McGilchrist –attention and the divided brain

43. 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)

44. 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.)

45. 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)

46. 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

47. 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.

48. 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)

49. 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)

50. 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.

51. Overlying cortex
Hippocampus
Neural activity level
via c-Fos expression
Validation 2: Recruitment of deep brain structures

52. 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

53. 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+

54. 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

55. Validation 3: Safety

56. 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.

57. 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)

58. Noresponse
**
*
*
*
Mean (± st.d.) Threshold vs.
Difference Frequency
Mean (± st.d.) Threshold vs.
Carrier Frequency

59. 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

60. 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)

61. 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

62. 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

63. 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

64. 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

65. 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.

66. 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

67. 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

68. 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

69. 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

70. 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

71. 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

72. 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

73. 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

74. 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

75. Explanatory levels of brain stimulation
Electro/Magneto-chemical Photo-chemical

76. 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.

77. Photobiomodulation
• Intensity?
• Frequency?
• Location?
• Stimulation duration?
• Brain state? (task, no-task)

78. PMB-EEG study
“Targeting several location at once”
transcranial PBM Default mode network hubs
Intranasal PBM
Does it really work ?! $$$

79. Exploring the effect of Photobiomodulation(PMB) on cortical
oscillation using EEG

80. Double-blind study on 20 Healthy control

81. 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

82. PBM-EEG study: Connectivity analysis

83. 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

84. 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

85. 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

86. 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.

88. Access recorded talks, Q&A, and more at:
SharpBrains.com