Diffusion functional-MRI (dfMRI) is thought to capture microstructural changes associated with neural activity. The water apparent diffusion coefficient decrease observed upon neuronal activity is hypothesized to be cell-swelling dependent. Yet, one of the confounding factors for dfMRI is that zero and nonzero b-values need to be acquired to deliver accurate changes in diffusivity and those images are typically separated by at least one repetition time. Incomplete initial Nutation Diffusion Imaging (INDI) was proposed as a method to acquire two images with different diffusion-weighting with separation of <50ms. Here, we performed INDI-fMRI experiments to report mean diffusivity changes using forepaw-stimulated rats.
Animal preparation: All experiments were preapproved by the local animal ethics committee operating under local and EU laws. Long Evan rats, 8-10 weeks old (n=11), were induced into deep anesthesia with 5% isoflurane and maintained under 2.5% isoflurane while two needles were inserted into the left forepaw’s digits 1-2 and 4-5. Animals were then switched to medetomidine sedation (bolus: 0.05mg/kg, constant infusion: 0.1mg/kg/h).
Stimulation paradigm: Following 45 seconds of rest, electrical pulses were delivered to the forepaw with a square waveform comprising 1.5mA, 10Hz and 3ms stimulus duration, for 15 seconds followed by 45 seconds of rest for a total of 6 stimulation periods (Figure 1A).
Functional Imaging:All experiments were performed using a 9.4T Bruker BioSpec scanner equipped with a gradient system producing up to 660 mT/m isotropically. An 86 mm quadrature resonator was used for transmittance, while a 4-element cryoprobe was used for reception. Four coronal slices of interest were scanned between +1.68 and -4.36 mm from bregma. The INDI sequence4 (Figure 1B) was applied in fMRI mode using the following parameters: TR/TE=1500/40 ms, FOV=15.6 x 12.0 mm, matrix size of 78x60, partial Fourier 1.33, interpolation in read direction 1.05, slice thickness 1.5 mm, in-plane resolution 200µmx200µm). Pairs of images were acquired with b value pairs of: [0,1000], [500,1000], [1000,1500] and Δ/δ = 19.5/14.2 ms, b tensor [zeta,theta,phi] = [54.7,0,0] deg, respectively.
Data analysis:Images were spatially realigned using SPM in Matlab (The Mathworks, Nattick, USA). Then, regions of interest (ROIs) were drawn in the forelimb primary somatosensory cortex (layer IV). Mean±standard error of the mean signals were extracted and drift-corrected. The average temporal evolution was calculated by averaging all stimulation epochs from all animals. Activation maps were calculated by performing spectral analysis, based on Fourier and harmonics analyses5. Functional maps of the MD data was smoothed using a gaussian filter with a kernel of 0.4x0.4mm2. All anatomical information was inferred from the Paxinos&Watson rat brain atlas6.
1. Tsurugizawa T, Ciobanu L, Le Bihan D. Water diffusion in brain cortex closely tracks underlying neuronal activity. Proc Natl Acad Sci U S A. 2013;110(28):11636-11641.
2. Abe Y, Tsurugizawa T, Le Bihan D. Water diffusion closely reveals neural activity status in rat brain loci affected by anesthesia. PLoS Biol. 2017;15(4):e2001494.
3. Jasanoff A. Bloodless FMRI. Trends Neurosci. 2007;30(11):603-610.
4. Ianus A, Shemesh N. Incomplete initial nutation diffusion imaging: An ultrafast, single-scan approach for diffusion mapping. Magn Reson Med. 2017.
5. Nunes D, Ianus A, Shemesh N. Layer-specific connectivity revealed by diffusion-weighted functional MRI in the rat thalamocortical pathway. Neuroimage.2018;184:646-657.
6. C.Watson GP. The Rat Brain in stereotaxic coordinates.7th edition ed: Academic Press; 2014.
7. Van Camp N, Verhoye M, Van der Linden A. Stimulation of the rat somatosensory cortex at different frequencies and pulse widths. NMR Biomed.2006;19(1):10-17.
8. Veraart J, Fieremans E, Novikov DS. Diffusion MRI noise mapping using random matrix theory. Magn Reson Med. 2016;76(5):1582-1593.