Localizing Function-specific Targets for Transcranial Magnetic Stimulation in the Absence of Navigation Equipment

I work in motor cognitive neuroscience, looking at how to boost fine hand movements with TMS. We are trying to find the simple ways to target the brain function even without navigation systems.

Transcranial magnetic stimulation targeting has gone from simple spot picking to tuning brain networks and with AI helping, it's getting smarter, more personal, and closer to custom brain treatments.

Right now, ways to define transcranial magnetic stimulation targets include multi-modal imaging guided, FMRI-guided, peaking targets based on cognitive performance using closed loop and the brain state-dependent TMS and modeling electric fields with high precision.

It is challenging to map surgical cortical coordinates and the two-disc cap with introducing errors.

Most colleagues don't have neural navigation system, so they cannot solve for personalized function-specific transcranial magnetic stimulation treatment. Our approach choose solve this problem.

[Presenter] To begin, open the pre-processing software. Click on DPARSF 5.4, then choose the DPARSF Advanced Edition to pre-process the task state data using specific parameters. Perform slice timing and head motion corrections. Coregister the functional images to structural images and apply spatial smoothing with a full width at half maximum of six millimeters. Open SPM 12 and click on coregister estimate. For the reference image, select the file named sub-asterisk crop_1.nii from the T1 Img folder. For the source image, choose the mean asterisk.nii file from the realign parameter folder. For the other image, select the raw asterisk.nii file from the fun Img AR folder. Click on segment and then select volumes. Choose the sub-asterisk crop_1.nii file from the T1 Img folder. For deformation fields, select inverse plus forward. Then click run. Repeat this segmentation for the sub-asterisk.nii file from the same folder. Next, click on smooth. Select the raasterisk.nii files from the fun imgar folder for image to smooth and enter 666 in the FWHM field. Perform first-level analysis to obtain individual activation maps and identify the peak voxel of activation as the stimulation target. Create a new folder named indiv_act and click on specify first level. In the directory field, select the indiv_act folder. Click on units for design, choose scans, and enter two for the interscan interval. In the data and design section, choose the SRA asterisk.nii files under scans. Under the condition section, set name to tap. Then enter 0, 30, 60, 90, for onset and set durations to 15. Then click on multiple regressors and select the rp_aasterisk.txt file from the realigned parameters. To estimate, select the SPM.map file from the indiv_act folder and generate the individual task activation map, spmt_0001. Now, press results and choose the spm.map file from the indiv_act folder. Check T contrast and click on define new contrast. Enter a custom name, then input 1,0 in the contrast field and click on submit. Okay, done. Under apply masking, select none. Then choose none under P value adjustment to control and set the value to 0.001. Set the and extend threshold to zero. Now, click on normalize write and then data. In deformation fields, select the iy crop 1 file from the T1 Img folder. For image to write, choose the M1 brain region mask. Then enter the individual bounding box and voxel sizes. Next, press coregister reslice, then select SPMT_0001 from the indiv_act folder for image defining space. For image to reslice, choose the W asterisk.nii file generated previously. Now, compute the individual task activation peak. In MatLab, run the sort positive code, then input names as given. Identify the first X coordinate with a negative value and record it as the individual task activation peak. To locate the individualized, function-specific, target launch SPM 12, click on FMRI and then select segment from the menu. Under the parameters interface, press the volumes button, select the MNI brain template file. Then click on deformation fields to select inverse plus forward. Next, launch MatLab and run the edges code to outline the inner and outer edges of the standard scalp. Select the c5.nii image. Then click done to generate the c5_edges.nii file. Now, use SPM 12 to transform the standard scalp edge into individual space. Click normalize write and click on data. Select the iy_sub asterisk.nii file from the T1 Img folder under deformation fields. Choose C5 outer edge.nii for images to write and input the individual bounding box and voxel sizes. Convert cortical coordinates to scalp coordinates by opening the transcortex to scalp code in MatLab and execute the first line. Enter the individual activation point coordinates. Select the WC5 outer edge file. Then record the output coordinates. Open dpabi viewer. Click on underlay and select the individual T1 structural image. Locate and record the coordinates of the left and right auricular peaks, the nasion, and the inion. Now, define the scalp origin by opening the intersection code in MatLab. Input the coordinates of the four landmark points. Then run the code to calculate the intersection coordinates of the ear and nasion-inion lines. Record the intersection coordinates. To move the intersection point along the Z axis to the scalp, open the origin code in MatLab. Enter the intersection point coordinates in define point H and select the WC5 outer edge file. Obtain the scalp origin coordinates O. To calculate the actual distance from the scalp origin to each point, run the distance code. Select the WC5 outer edge file and enter the scalp origin, target, and four landmark point coordinates as prompted. Now, calculate the angle between the line connecting the scalp target and the scalp origin and the X axis in the XY plane by opening the calculate angle X axis code and run the first line. In the command window, input the coordinates of the scalp origin and stimulation target. Use the targeting ruler to fix the corresponding soft ruler position based on the calculated distance and angle. Then mark the scalp with a washable pen. Based on one sample T-test maps, functional connectivity and amplitude of low-frequency fluctuation results are displayed without multiple comparison correction.