(E-173) Evaluating Low-Intensity Pulsed Ultrasound as a Therapy for TBI using DTI-based Tensor Based Morphometry- A Longitudinal Study in Rodents

Introduction:: Traumatic Brain Injury (TBI) is a devastating condition that impacts approximately 69 million people (about twice the population of California) worldwide (WHO). Due to its long-term symptoms such as seizures because of TBI, which may be due to untreated inflammatory processes in the brain, low-intensity pulsed ultrasound (LIPUS) has been viewed as a potential way of reducing inflammation and promoting tissue repair. Little is still understood about LIPUS and more studies that evaluate its use are necessary for assessing its effectiveness as a treatment of TBI. Our aim is to evaluate LIPUS as a therapy for TBI longitudinally with diffusion tensor imaging (DTI), one of the many imaging types that can be used in Magnetic Resonance Imaging (MRI).

Materials and Methods:: A total of 18 mice (10 male, 8 female) were imaged longitudinally at baseline, 24 hours post-TBI, and 4 weeks post-TBI. Diffusion measurements were performed on Bruker Pre-clinical 7T MRI (30 directions; b value= 800 mm/s2; resolution=0.25x0.25x0.3 cm). LIPUS treatment for acute and chronic were kept consistent (frequency=2.25 MHz, amplitude= 100 mW/cm2, 50% duty cycle, burst period= 100 Hz, sonic duration= 15 minutes) except for number of sessions and when they began after injury (acute – immediately after TBI and 5 sessions across total of 5 days; chronic – 1 week after TBI and 10 sessions total of 14 days) (shown in fig. 1A). Data, analysis and statistical analysis were performed using a combination of linux bash scripts (diffusion metric processing and template creation)1-4 and python (for region of interest analysis). Regions of interest (ROIs) of the ventricles were extracted using a threshold of 2250 on the baseline Trace (TR) template and the corpus callosum ROI was extracted using a pre-existing scalable brain atlas that was co-registered to our baseline template5. Template was created using baselines from the cohort and all longitudinal timepoints were then registered to a shared template space1-4,6. MR metrics analyzed included log of the jacobian (logJ) to evaluate morphometric changes between post-TBI timepoints to baseline, Fractional Anisotropy (FA) to evaluate geometric changes in the organization of white matter, and TR to evaluate inflammation and edema.

Results, Conclusions, and Discussions:: An initial pairwise t-test was run to evaluate TBI versus sham and a following pairwise t-test to evaluate TBI vs. Chronic LIPUS and Acute LIPUS. From this analysis, there was no significant difference in FA or TR values between sham and TBI in the ipsilateral or contralateral side of the corpus callosum. A significant difference between the LogJ of the ventricles and paraventricular space was found when comparing sham and TBI at 4 weeks on the ipsilateral side (p=0.036, cohen’s D= 2.5; p=0.027, cohen’s D=2.01) shown in fig. 1B and fig. 1C. No significant differences between LogJ were observed in the corpus callosum or in any of the LIPUS groups at the different timepoints. These results suggest that the TBI more severely affected the ventricles and surrounding ventricle tissue on the side of injury and did not disrupt the white matter integrity based on the resolution of our imaging parameters. LIPUS treatment was not observed to modify this change in increased ventricle size from TBI. Additionally, the lack of significance in FA and TR in the corpus callosum may be due to the low number of animals in the study or that the effects of the injury were more local. Further analysis of data collected ex-vivo of the same study will provide additional information regarding white matter integrity changes at higher resolution.

Acknowledgements (Optional): : All imaging was performed in the UA translational bioimaging resource (TBIR) and made possible by the NIH small instrumentation grant: S10 OD025016.

References (Optional): :

1. 
   1. C. Pierpaoli, L. Walker, M. O. Irfanoglu, A. Barnett, P. Basser, L-C. Chang, C. Koay, S. Pajevic, G. Rohde, J. Sarlls, and M. Wu, 2010, TORTOISE: an integrated software package for processing of diffusion MRI data, ISMRM 18th annual meeting, Stockholm, Sweden, abstract #1597
   
   

2. 
   2. Mustafa Okan Irfanoglu1,2,3, Amritha Nayak1,2,3, Jeffrey Jenkins1,2, and Carlo Pierpaoli1,2 ,TORTOISEv3:Improvements and New Features of the NIH Diffusion MRI Processing Pipeline, ISMRM 25th annual meeting, Honolulu, HI, abstract #3540
   
   

3. 
   3. Irfanoglu MO, Modi P, Nayak A, Hutchinson EB, Sarlls J, Pierpaoli C.DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions, Neuroimage. 2015 Feb 1;106:284-99. doi: 10.1016/j.neuroimage.2014.11.042. Epub 2014 Nov 26.
   
   

4. 
   4. Irfanoglu MO, Nayak A, Jenkins J, Hutchinson EB, Sadeghi N, Thomas CP, Pierpaoli C, DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures. Neuroimage. 2016 May 15;132:439-454. doi: 10.1016/j.neuroimage.2016.02.066. Epub 2016 Feb 28.
   
   

5. 
   5. Adhwar A, Lerch JP, Hamel E, Sled JG (2013) "Impaired structural correlates of memory in Alzheimer's disease mice" NeuroImage: Clinical 3:290-300. [doi 10.1016/j.nicl.2013.08.017]
   
   

6. 
   6. Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC. The Insight ToolKit image registration framework. Front Neuroinform. 2014 Apr 28;8:44. doi: 10.3389/fninf.2014.00044. PMID: 24817849; PMCID: PMC4009425.