development of implantable interfaces to restore motor ... 11.21.19 - ganguly...no-stim s1 s5...
TRANSCRIPT
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Development of Implantable
Interfaces to Restore Motor Function
Karunesh Ganguly, MD PhD
Osher Mini Medical School “UCSF Scientists Outline What’s to Come”November 22, 2019
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Introduction• Concept of bio-interactive neural
interfaces dates to early 20th century
• Successful translation of − Cochlear implants− Deep brain-stimulation (DBS)− Responsive stimulation (RNS)
• Neural Interface for paralysis and rehabilitation
− ‘Brain-Machine Interfaces’/’Brain-Computer Interfaces’
MECHANIX ILLUSTRATED (1957)
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3
Cochlear Implants
• Auditory nerve stimulation research starting in the 1950s
• ~22,000 adults and ~15,000 children live in the US with cochlear implants
Image credit: UCSF Benioff Children’s Hospital
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4
Deep-Brain Stimulation
Source: NIH, Neurology
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Neural Interfaces for Communication and Movement
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Motor Disability in the US
www.cdrf.org
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Locked-In (e.g. TBI, Stroke, ALS)
Upper Limb paralysis
VS/MCS (cardiopulmonary arrest, TBI, etc)
Quadriplegia, SCI (CP,ALS, MS, etc)
Comatose
Rehabilitation Needs Vary
7
Paraplegia
Below ~C5/C6
Above ~C5
Loss
of I
ndep
ende
nce
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Patient Rehabilitation Goals
Anderson, J Neurotrauma (2004)
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Motor Dysfunction
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Brain-Computer Interface (BCI)
Move left..
Also known as “Neural Interface” and “Brain-Machine Interface/BMI”
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Extracellular Recording of Activity
11
Spikes Electrode
Neurons
ECoGElectrode
Field potentials
Spikes
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Recording neural activity from the brainfMRI, MEG
Schwartz et al. Neuron, 2006
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Recording neural activity from the brain
Buzsaki et al., Nat Neuro Reviews, 2012
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Principles Underlying BCI Control
14
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Volitional control of brain activity
Fetz, E, Science, 1969
1 cm
M1
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16Georgopoulos AP, J. Neurosci (1982); Dum & Strick (2002); Schwartz, J. Physiology (2006)
Volitional control of movementsA B
BCIs grounded by > 40 years of research into movement control!
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1 cm
PPSMA
PMdS1 M1
Distributed neural population activity
From Wessberg et al., 2000; but see also Chapin et al., 1999, Serruya et al., 2002; Taylor et al., 2002; Carmena et al., 2003; Velliste et al., 2008; Pohlmeyer et al., 2009
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Recording from neural populationsM1 (L)M1(R) PMd (L)
1.2mVpp
0.5mVpp]
1.3mVpp
0.3mVpp
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Real-time decoding of hand position
Ganguly et al., 2009
Actual hand movementsDecoder predictions
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“Closed-Loop” Brain-Computer Interfaces
Neural Signals
“Actuator”• Prosthetics• FES• Stimulation
“Decoders”
Feedback
Sensors
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Neural plasticity key for stable BCI control
Ganguly et al., PLoS Biology, 2009; Ganguly et al., Nat Neuro 2011; Gulati et al, Neuro Neuro 2014, 2017 Nat Neuro; Kim et al., Cell 2019
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Examples of BCI Control in Human Subjects
22
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Development of BCI Control
• Real-time recording of ensembles Chapin et al., 1999; Wessberg et al., 2000
• Multiple demonstrations of ‘closed-loop’ control over an external deviceSerruya et al., 2002; Taylor et al., 2002; Carmena et al., 2003; Velliste et al., 2008; Pohlmeyer et al., 2009
• Human subjects can learn direct neural control of a computer cursorKennedy et al., 2000; Leuthardt et al., 2004; Hochberg et al., 2006
• Clear demonstration that a subject’s intentions can be translated
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EEG-interface for communication• Two patients with advanced ALS (ventilated, PEG tube for > 4
years) could learn to communicate• Patients were ‘locked-in’ (no voluntary muscle movements)• EEG signals could be used to type a message (but very slow!)
Birbaumer et al., Nature (1999)
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Case Report: A 51yo molecular biologist with ALS…
25Sellars et al., Amyotroph Lateral Scler. (2010)
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BCI Control of a Computer Cursor
26FROM Collinger et al., Lancet (2013)See also Hochberg et al., 2006, 2012; Wodlinger et al., 2014
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BCI Control in a tetraplegic subject
FROM Pandarinath et al., eLife(2017).
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Translational Challenges
28
• Signals are not stable à Utah Array
• Daily training required because of signal stability
• Complex setups that require lot of support
• Currently not wireless (this will be solved soon)
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Ongoing UCSF studies- ECoG BCI Trial- Neural Interfaces for Stroke
29
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ECoG based chronic implant
30** Experimental Device à FDA Investigational Device Exemption (IDE)
• FDA cleared testing of a chronic ECoG based device
• Addresses a downside of current trials• Signal instability• Daily training
• Two primary goals are control of a typing interface and a complex robotic arm
• Leverage stability of ECoG signals to engage learning and plasticity
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‘Plug-and-Play’ BCI Control
31
‘Neural maps’ Across Days
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Impaired hand function after stroke
• ~700,000 strokes/year
• >$15K for rehabilitation per patient– Limits of rehab (ICARES Trial, 2018)
• ~50% with hand impairments, limits independence
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Towards the Development of a Closed-Loop Neural Interface for Stroke
Neuralsignal
EMG
Stimulation
What is required for closed-loop modulation?- Electrophysiological targets?- Predictor of good/poor recovery?- Can targeted stimulation help chronic deficits?
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Synchronous Network Activity During Skilled Movements
Lemke et al., Nature Neuroscience, 2019
M1
DLS
M1
DLS
Muscle
M1
DLS
Time (sec)-1 1
Neur
ons
Neur
ons
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Changes in Speed/Consistency with Learning
1
Tria
l #
600Accuracy (%)
Time (sec)
0 70
0 1.2
M1-DLS Coherence
Lemke et al., Nature Neuroscience, 2019
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Reemergence of synchronous activity
0 100Accuracy
Tria
l #
250
Accuracy (%)Tr
ial #
260
1
0Time (s)
-.5 1.5
PLC electrode
Stroke
1.5-4 Hz LFP PowerReach
Ramanathan*, Guo*, Gulati* et al. Nature Medicine (2018)
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Responsive Stimulation
37
Onset ofmovement
Limb position
Temporal electrical stimulation
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Session
Stim (400 μA)No-stim
S1 S5
Accu
racy
(%) 75
25
Responsive Stimulation
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SummaryInjured CNSIntact CNS
VA/VL
DLS
PLC
GPi
GPe
M2
DMS
S1/S2
Cerebellum
STROKE
Limb positionSpikes
Field Potential
Neural Activity
Temporally precise stimulation
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ECoG in Stroke
40
Fugl-Meyer of 35
Ramanathan*, Guo*, Gulati* et al. Nature Medicine (2018)
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Reduced task-related slow-oscillations
41
PLC electrode
Stroke
a
Elec
trode
im
plan
t +
PT s
troke
Time (sessions)
Reach onlyReach with neural recording
b c
B S1 S10
B = BaselineS = Session # Ac
cura
cy (%
)
100Stroke
0
d
e gf
r = 0.52***-10
80
-0.2 0.8Power change (z-sc)
Succ
. cha
nge
(%)
h
0.2
Time from reach (s)-1 0 1.50.08
SFC
EarlyLate
1.5-4Hz SFC
i j
k l
1.5-4Hz power
2.5
-0.25
Freq
. (H
z)
1.5
6
-0.05 0.45Time (s)
Early power spatial map
Late power spatial map
0.05
Time from reach (s)
20
-1
15
10
5
1.5 0 1.5
Freq
uenc
y (H
z)
-1 0 1.5
0.2
SFC
Early SFC Late SFC
-0.2
Time from reach (s)
20
-1
15
10
5
1.50 1.5
Freq
uenc
y (H
z)
-1 0 1.5
0.6
z-sc
ore
Early power Late power0.5
Time from reach (s)-1 0 1.5-0.1
z-sc
ore
Time from reach (s)
Uni
ts
-1 1.50
Firin
g ra
te (z
-sc)
-2
4
-1 1.50
Early unit firing Late unit firing1 1
170 219
Time from reach (s)-1 1.50
LFP
Uni
tsPE
TH
RetractReachGrasp1
2630
01.2
-1.2
Tria
l #
0-1 1.5Time from reach (s) Succ. (%)
0 100
S1
S11
1
255
1.5-4Hz Power
RatHuman
Ramanathan*, Guo*, Gulati* et al. Nature Medicine (2018)
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Thanks for your attention!
42
Research Funding