Mayank V Jog1, Robert X. Smith2, Kay Jann2, Walter Dunn3, Allan Wu2, and Danny JJ Wang2
1Biomedical Engineering, University of California Los Angeles, Los Angeles, CA, United States, 2Neurology, University of California Los Angeles, Los Angeles, CA, United States, 3Psychiatry, University of California Los Angeles, Los Angeles, CA, United States
Synopsis
Transcranial Direct Current Stimulation(tDCS) is a
neuromodulation technique. Reported to improve clinical conditions as well as
cognition, tDCS has potential as a treatment modality since it involves only
simple scalp electrodes to drive mA currents. To date, only mathematical
modeling has been used to visualize tDCS-applied currents.
In previous work, we used MRI field mapping in a
novel paradigm to visualize in-vivo, a component of the magnetic field generated
by these currents. The present work completes the picture by validating our
current visualization technique via comparison between the measured and
simulated current-induced fields in a specially constructed phantom.
PURPOSE
Transcranial
direct current stimulation (tDCS) is an emerging non-invasive neuromodulation
technique that has been shown to improve symptoms in a range of neurologic and
psychiatric disorders (epilepsy, Parkinson’s disease, chronic pain, depression
and stroke [1,2,3]). Determining the spatial distribution of the applied
current is critical to evaluate targeting and improve efficacy of tDCS montages.
A MR current mapping technique has recently been introduced for visualizing
tDCS induced electromagnetic field changes based on Ampere’s law [4,5]. The
goal of the present study was to validate the MR current mapping technique in a
conductive phantom model through comparison with theoretical calculations based
on the Biot-Savart law.
METHODS
Ampere’s
Law states that an applied direct current (DC) induces a magnetic field with
its magnitude proportional to the current intensity and its direction
orthogonal to the current. The along-B0 component of this induced
field can be detected as phase changes using MRI field mapping. A field mapping
experiment was performed using a cylindrical phantom fitted with an electrolyte
filled plastic U-shaped tube (‘A’) (Fig
1). All applied currents were confined to this tube. An identical U-shaped tube(‘B’)
was included as a control. Currents (0.5-1.5mA) were applied in a pseudo random
order (Fig 2) over three sessions:
‘Active’, ‘–Active’ and ‘Sham’. The polarity of currents in ‘–Active’ was reversed
relative to ‘Active’, while currents were switched off during ‘Sham’. Field
mapping data was acquired concurrently with each applied current using a
standard field mapping sequence (Quadrature volume coil, field mapping
TE1/TE2=4.92/14.76 msec, TR=1.15 sec, FA = 250, 65 slices, 2x2x3mm3
Voxel, Matrix: 128 x128, BW=750 Hz/pix) on a Siemens 3T PRISMA scanner. With
these parameters, the minimum detectable field was calculated as ~0.6nT/mA.
Analysis:
Acquired phase data was SNR thresholded to preserve voxels with Gaussian noise
[6]. Data was subsequently unwrapped using the Region Growing algorithm (implemented
in PhaseTools [7]). Phase was modeled as:
Φm = Φ0 + ΦCurrent + ΦNon-Current +
ΦDrift + ΦNoise
where Φm is the measured phase, Φ0 is the baseline phase,
ΦCurrent is phase due to current-induced fields, ΦNon-Current
is phase due to non-current sources (e.g. off resonance), ΦDrift represents
the MRI inter-scan field drift and ΦNoise is Gaussian noise.
Unwrapped data was modeled voxel-wise using a general linear model (GLM) with
applied currents as predictor. By using the phase difference ΔΦm between
two TE’s in our analyses, Φ0 was eliminated. ΔΦNon-Current
by definition does not vary across currents, and is modeled by the intercept. ΔΦDrift
was modeled by interpolating a polynomial to the zero current scans. The degree
of the polynomial was adapted to avoid overfitting. The regression coefficient
of the predictor (applied current) represents the induced-phase at unit
current, and was converted to induced-magnetic field at unit current.
Simulations:
The induced magnetic field was simulated from current density using a finite
element implementation of the Biot-Savart law. The current density in turn was
calculated from the geometry of Tube ‘A’ and the assumption of the electrolyte
conductivity being uniform and isotropic. It should be noted that while the
Biot-Savart law predicts the magnetic field at a point, MRI measures the
induced field averaged over a voxel. Our simulation addresses this by averaging
the induced magnetic fields over 175 equi-spaced points within each voxel.
RESULTS
Figure 3a
shows the simulated current-induced field. Induced magnetic fields as low as 5nT/mA
were reliably detected in the ‘Active’ session (Fig. 3b), and were highly consistent with simulations. The induced
fields in the ‘–Active’ session(Fig. 3c)
were observed to be similar in magnitude, while opposite in sign. This is
expected, since reversing the current polarity reverses the direction of the
induced fields (Ampere’s Law). No current-induced fields were detected for the
‘Sham’ session (Fig. 3d), or in the
intra-session control tube ‘B’.
DISCUSSION and CONCLUSION
Our
results provide a validation of the MR current mapping technique for in vivo
studies. Through the phantom experiment, we demonstrated reliable detection of current
induced field changes as low as ~5nT/mA.
Our technique also demonstrates excellent specificity: No fields were detected
in the ‘Control’ session or the intra-session control (Tube ‘B’). One unique
advantage of our technique is that it uses information from phase images. Traditional
fMRI techniques mapping neurophysiological markers mostly utilize magnitude
images. Since every MRI acquisition measures a phase and a magnitude image, it
is conceivable that the two techniques may be combined to enable simultaneous
measurement of applied current and the brain response from a single MRI
experiment.
Acknowledgements
No acknowledgement found.References
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