Resting state fMRI changes during Spinal Cord
Stimulation
Chima O.Oluigbo, MD, Amir Abduljalil, PhD,
Xiangyu Yang, PhD, Andrew Kalnin, MD,
Michael V. Knopp, MD, PhD, Ali R. Rezai, MD
Center for Neuromodulation, Departments of
Neurosurgery and Radiology, Wexner Medical
Center at The Ohio State University Hospital
Disclosure
• No personal disclosures
• Funding by Medtronic
Background – Chronic Pain
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70 million Americans, $150 billion per annum,
Develop innovative therapies
New methods to evaluate and characterize pain
Cerebral “signature” for pain perception and
modulation
• Neural network changes – depression, addiction
Farmer et al. Neuroscience Letters 520 (2012): 197-203
Resting State fMRI
• Allows interrogation of myriad
functional systems without the
constraints of a priori hypothesis
• Imaging the brain during rest reveals
large-amplitude spontaneous lowfrequency (<0.1 Hz) fluctuations
• Temporally correlated across
functionally related areas
• “Functional connectome”
• Default mode network
Lateral parietal cortex (LPC)
Posterior cingulate/
Precuneus (PCC)
DEFAULT MODE NETWORK
Medial prefrontal cortex (MPC)
Clinical model – Neuropathic extremity
pain and spinal cord stimulation
Design Overview
• OSU IRB approved research study
• 7 patients
• Thoracic epidural SCS in place for treatment of
CRPS or neuropathic leg pain following FBSS
involving one or both lower extremities
Pre-Imaging Clinical evaluation
• Determine stimulation parameters associated with:
1. SCS Perception threshold
2. “Optimal” pain reduction
3. Uncomfortable stimulation threshold
Pain Quantification
Pain quantification was based on the Visual-Analog Scale (VAS)
and the measure of percentage change in pain (∆P%) was
determined as follows:
∆P% = 100x (POFF – PON)/POFF
where PON is the VAS pain rating as reported by the subject
during stimulation while POFF is the pain rating reported with
the stimulator switched OFF.
MRI safety
• Under OSU IRB approved research study, modeling
analysis and laboratory measurements were
performed
• Determined that the Neuromodulation devices would
perform safely under the restrictions of this
particular research protocol, MRI equipment, and
implant restrictions.
• Note: cannot be extrapolated to other studies or other
systems
fMRI protocol
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7 subjects
1 control – 5 sessions of resting fMRI on different days
Resting state fMRI
3 T Achieva Philips scanner, transmit /receive head coil.
Functional EPI images acquisition: isotropic spatial resolution of 3 mm,TR/TE
2000/30 ms, 80° flip angle, 80×80 matrix size, 35 slices.
B0 field map and a high resolution 3D T1 weighted image also acquired: TR/TE
7.9/3.7 ms, 1×1×1 mm3 voxel resolution.
Image analysis using FSL (FMRIB Software) and AFNI (NIMH/NIH) tools.
Functional images were motion corrected, smoothed (5 mm3) and band-pass
filtered (0.005<f<0.1 Hz).
– 10 minutes scans
– Simulation
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Off
Low
Optimum
High
Image preprocessing
•Frequency-domain analysis
Computing ALFF (Amplitude of Low Frequency Fluctuation)
•Seed-based functional connectivity
•Independent component analysis (ICA)
1
OFF
Similarity coefficient η2
Spatial normalization
1
Group region based analysis
•Frequency-domain analysis
•Seed-based functional connectivity
•Independent component analysis (ICA)
Optimum
Results 1: Pain change calculations
∆P% = 100x (POFF – PON)/POFF
Subject
∆P% (Optimum)
∆P% (Supra-optimal)
1
40%
100%
2
0%
-16.6%
3
29.4%
41.2%
4
71.4%
71.4%
5
50%
57.1%
6
27%
63.6
7
75%
100%
Frequency Domain Analysis – Amplitude of
Low Frequency Fluctuation(ALFF)
• ALFF represents the average amplitude in the low-frequency band (0.01–
0.08 Hz).
• Reflects the intensity of regional spontaneous brain activity
• Calculated by averaging the square root of the power spectrum of a given
low-frequency BOLD time course across the frequencies filtered
• The fALFF shows the ratio of power spectrum of low-frequency (0.010.08 Hz) to that of the entire frequency range. It is inverse to ALFF
ALFF
Chronic pain – Stimulator OFF (Group summation, n = 7)
Normal control (n = 5)
-4.5
4.5
fALFF
Chronic pain – Stimulator OFF (Group summation)
Normal control
-4.5
4.5
Group ALFF
Similarity coefficient with stimulation at different
parameters.
0 = no similarity, 1 = identical
OFF
Similarity coefficient threshold : Task based 0.5
Resting state 0.35
Low
Opt
High
Global Similarity coefficient
0 = no similarity, 1 = identical
Threshold ≤ 0.35
Seed based correlation analysis
Involves the a priori selection of a voxel,
cluster or atlas region and then calculate
whole-brain, voxel-wise functional
connectivity maps of co-variance with the seed
region.
Pain related seeds
R DLPFC (right dorsolateral prefrontal cortex)
L DLPFC (left dorsolateral prefrontal cortex)
FMC (Frontal medial cortex = Medial orbitofrontal)
LFI (Left orbital frontoinsula = Left anterior insula)
RFI (Right orbital frontoinsula = Right anterior insula)
LAccu (Left nucleus accumbens)
RAccu (Right nucleus accumbens)
LAmyg (Left amygdala)
RAmyg (Right amygdala)
LPIN (Left posterior insula)
RPIN (Right posterior insula)
RACCX (Right Anterior Cingulate Cortex) = RCC
LACCX (Left Anterior Cingulate Cortex)
44 36 20
-34 31 34
0 42 -18
-32 24 -10
38 26 -10
-10 12 -8
10 10 -8
-20 -6 -20
28 -6 -20
-39 -24 16
38 14 6
6 38 14
-2 36 6
Task positive seeds
IPS (Interparietal sulcus)
FEF (Frontal eye field)
MT (Middle temporal)
-38 -46 54
26 -12 50
-46 -68 -2
Default Mode Network Seeds
MPF (Medial prefrontal cortex)
PCC (Posterior cingulated / precuneus)
LP (Lateral parietal cortex)
-2 46 -16
-4 -50 40
-46 -68 36
Right Anterior Insula (Off/Opt)
Leftt Anterior Insula (Off/Opt)
Left Amygdala (Off/Opt)
Right Amygdala (Off/Opt)
Structural Equation Modeling (SEM)
• Causality modeling approach
• Provide measure of effective
connectivity
• Model driven (ie ROI
dependent)
• Provide confirmation for
hypothesis testing
• SEM does not prove causation
Group Off
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Group Low
Group Optimum
Group High
Farmer et al. Neuroscience Letters 520 (2012): 197-203
Conclusions
• SCS influences supraspinal (cerebral) pain
neuromodulation – indirect / direct
• Pain control during spinal cord stimulation is
associated with change in connectivity between
anterior insula (and amygdala) and components of the
default mode network (DMN)
• ALFF in the region of the DMN is lower in patients
with chronic pain compared to control.
• Spatially correlated fluctuations in resting state fMRI
signals may be a neuroimaging surrogate for higher
order pain perception and its modulation in chronic
pain states
Thank you