Event Abstract Back to Event Longitudinal DTI changes following cognitive training therapy in a mild traumatic brain injury rat model Kim Braeckman1*, Benedicte Descamps1, Karen Caeyenberghs2 and Christian Vanhove1 1 Medical Imaging and Signal Processing, Ghent University, Belgium 2 Centre of Disability and Development Research, Australian Catholic University, Australia Summary in layman's terms Executive functioning (withhold and stop actions, and divide, switch and sustain attention) is important in everyday functioning but can be impaired after sustaining traumatic brain injury. Cognitive training therapy is used in recovery but the effect on the brain structure is unknown. In this study we investigated cognitive training induced brain changes with advanced magnetic resonance imaging in a rat model. We found that type of training (memory vs attention) has an influence on the MRI metrics and that training on attention has more promise at improving MRI metrics. These findings may be helpful in developing better cognitive training programs. Introduction Traumatic brain injury (TBI) is the leading cause of acquired disability in young adults, often caused by traffic accidents or sport injuries (Majdan et al., 2016). The majority of TBI patients (about 80%) suffer from mild TBI (mTBI). While conventional CT or anatomical MRI are not sensitive to map the diffuse and subtle injuries in mTBI, the patients can suffer from cognitive defects years after their injury. Deficits in executive functioning, such as difficulty in withholding and stopping actions; problems with sustained attention, can have a major impact on daily life activities, eg. attending meetings and briefings at work. It has been demonstrated that cognitive training therapies can ameliorate these difficulties (Stephens et al., 2015); however, the effect of a cognitive training program on tissue microstructure is far from understood in mTBI (For a review, see (Caeyenberghs et al., 2018)). Diffusion MRI metrics can provide quantifiable information of the brain microstructure and therefore provide much-needed mechanistic insights underpinning training-induced gains. Also, by making use of animal models we can unfold the mechanisms of cognitive rehabilitation in a controlled and standardized way and define how diffusion metrics are related to the underlying brain microstructure with immunohistological analysis. This can be helpful to further improve our understanding on how we can use a biologically-driven approach, to maximise cognitive outcomes with cognitive training paradigms. For example, Blumenfeld-Katzir et al. (Blumenfeld-Katzir et al., 2011) was able to detect training-induced changes in diffusion metrics in the dentate gyrus of healthy rats and could relate these to formation of astrocytic processes. In the present study we will characterise microstructural changes induced by cognitive training therapy using diffusion tensor imaging and evaluate whether the type of training (memory vs attention) has an influence on recovery following mTBI. Materials and methods Twelve female Wistar rats (276g +- 11g) sustained mTBI utilizing a weight drop model (Marmarou et al., 1994). One day after injury the 12 rats were randomized into two groups of cognitive training making use of the Bussey-Saksida operant touchscreen system (Lafayette Instruments)(Figure 1), i.e. 6 animals received the Paired associate learning (PAL, memory) regime and 6 animals underwent the 5-choice serial reaction time task (5-CSRT, attention). Animals were trained 5 days per week and this until the day before the last imaging time point. Before the start of the training period, the animals were weighed daily to establish a baseline body weight and during the training period the rats were food restricted making sure the body weighted was kept constant at 90% of their free-feeding body weight. Additionally, two animals (264g +- 4g) without TBI did not receive training and were used as passive healthy controls (Sham), with values only shown as reference. MRI data were acquired on a 7T MRI-scanner (Bruker) with a rat brain/mouse whole body volume coil before (baseline) and 1 day after injury, and during the training period after 1, 3, 6 and 9 weeks. The last scan session at 12 weeks is used as the post-training scan. T2-weighted images were acquired for anatomical reference. Multi-shell diffusion weighted images were acquired (b=800, 1500 and 2000 s/mm2; 32, 46 and 64 directions). DWI images were denoised in MRtrix3 (Veraart et al., 2016a, 2016b) and corrected for EPI, motion and eddy-current distortions in ExploreDTI 4.8.6. (Leemans et al., 2009). The diffusion tensor was estimated using a weighted linear least squares algorithm (Veraart et al., 2011) and maps for the diffusion metrics were calculated (Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD)). A volume-of-interest analysis was performed in the hippocampus because this region has shown alterations in diffusion metrics in previous training studies (Blumenfeld-Katzir et al., 2011; Sagi et al., 2012). The Wilcoxon signed rank test was performed to investigate training induced changes over time compared to baseline and the one day post injury time point. The Mann-WhitneyU test was performed with training group (PAL, 5-CSRT) as between-subject variable. P-values ≤ 0.05 were considered statistically significant. Results Compared to baseline, FA was increased one day post injury as a result of the mTBI. Following training therapy, FA returned towards baseline levels in both training groups, however more so in the 5-CSRT group (p1d-1w=0.028, p1d-6w=0.028, p1d-12w=0.046) (Figure 2A). This led to a significant difference in FA values between both groups one week and six weeks after injury (p=0.026 and p=0.026, respectively) (Figure 2B). Additionally, after training therapy, the FA values of the 5-CSRT group returned to values that were in line with the healthy controls, as can be seen in Figure 2B. Discussion Our results provide the first indication of training-induced plasticity in the hippocampus following mTBI in a rat model. Using diffusion MRI, we showed first an increase in FA caused by the mTBI (in accordance with unpublished data), followed by decreases in FA induced by training on the 5-CSRT task. These training induced decreases are in line with previous studies in healthy rats although using a short-term spatial memory task (Blumenfeld-Katzir et al., 2011; Hofstetter et al., 2013). We hypothesize that training-induced changes could be explained by a more complex microstructural organisation, for example caused by an increased activity of astrocytes and synapse formation. Ongoing histological analysis should confirm this hypothesis. Since we detected these changes only in the 5-CSRT group and the values of FA were in line of the sham group, we can be hopeful that the type of training (memory vs attention) can make a difference in the recovery process of mTBI. Figure 1 Figure 2 References Blumenfeld-Katzir, T., Pasternak, O., Dagan, M., Assaf, Y., 2011. Diffusion MRI of Structural Brain Plasticity Induced by a Learning and Memory Task. PLoS One 6, e20678. https://doi.org/10.1371/journal.pone.0020678 Caeyenberghs, K., Clemente, A., Imms, P., Egan, G., Hocking, D.R., Leemans, A., Metzler-Baddeley, C., Jones, D.K., Wilson, P.H., 2018. Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review. Neurorehabil. Neural Repair 32, 99-114. https://doi.org/10.1177/1545968317753076 Hofstetter, S., Tavor, I., Tzur Moryosef, S., Assaf, Y., 2013. Short-term learning induces white matter plasticity in the fornix. J. Neurosci. 33, 12844-50. https://doi.org/10.1523/JNEUROSCI.4520-12.2013 Leemans, A., Jeurissen, B., Sijbers, J., Jones, D., 2009. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proc Intl Soc Mag Reson Med 17, 3537. Majdan, M., Plancikova, D., Brazinova, A., Rusnak, M., Nieboer, D., Feigin, V., Maas, A., 2016. Epidemiology of traumatic brain injuries in Europe: a cross-sectional analysis. Lancet Public Heal. 1, e76-e83. https://doi.org/10.1016/S2468-2667(16)30017-2 Marmarou, A., Foda, M.A.A.-E., Brink, W. van den, Campbell, J., Kita, H., Demetriadou, K., 1994. A new model of diffuse brain injury in rats. J. Neurosurg. 80, 291-300. https://doi.org/10.3171/jns.1994.80.2.0291 Sagi, Y., Tavor, I., Hofstetter, S., Tzur-Moryosef, S., Blumenfeld-Katzir, T., Assaf, Y., 2012. Learning in the Fast Lane: New Insights into Neuroplasticity. Neuron 73, 1195-1203. https://doi.org/10.1016/j.neuron.2012.01.025 Stephens, J.A., Williamson, K.-N.C., Berryhill, M.E., 2015. Cognitive Rehabilitation After Traumatic Brain Injury. OTJR Occup. Particip. Heal. 35, 5-22. https://doi.org/10.1177/1539449214561765 Veraart, J., Fieremans, E., Novikov, D.S., 2016a. Diffusion MRI noise mapping using random matrix theory. Magn. Reson. Med. 76, 1582-1593. https://doi.org/10.1002/mrm.26059 Veraart, J., Novikov, D.S., Christiaens, D., Ades-aron, B., Sijbers, J., Fieremans, E., 2016b. Denoising of diffusion MRI using random matrix theory. Neuroimage 142, 394-406. https://doi.org/10.1016/j.neuroimage.2016.08.016 Veraart, J., Poot, D.H.J., Van Hecke, W., Blockx, I., Van der Linden, A., Verhoye, M., Sijbers, J., 2011. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging. Magn. Reson. Med. 65, 138-145. https://doi.org/10.1002/mrm.22603 Keywords: MRI – Magnetic Resonance Imaging, DTI (diffusion tensor imaging), cognitive training, TBI (Traumatic Brain Injury), rat Conference: Belgian Brain Congress 2018 — Belgian Brain Council, LIEGE, Belgium, 19 Oct - 19 Oct, 2018. Presentation Type: e-posters Topic: NOVEL STRATEGIES FOR NEUROLOGICAL AND MENTAL DISORDERS: SCIENTIFIC BASIS AND VALUE FOR PATIENT-CENTERED CARE Citation: Braeckman K, Descamps B, Caeyenberghs K and Vanhove C (2019). Longitudinal DTI changes following cognitive training therapy in a mild traumatic brain injury rat model. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00074 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Aug 2018; Published Online: 17 Jan 2019. * Correspondence: Ms. Kim Braeckman, Medical Imaging and Signal Processing, Ghent University, Ghent, East Flanders, Belgium, kim.braeckman@ugent.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Kim Braeckman Benedicte Descamps Karen Caeyenberghs Christian Vanhove Google Kim Braeckman Benedicte Descamps Karen Caeyenberghs Christian Vanhove Google Scholar Kim Braeckman Benedicte Descamps Karen Caeyenberghs Christian Vanhove PubMed Kim Braeckman Benedicte Descamps Karen Caeyenberghs Christian Vanhove Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.