CORTICAL CONNECTIVITY PREDICTS POST-STROKE DECLINES IN TRAIL MAKING PERFORMANCE Sarah Tryon 1, Olivia Spead 1, Addie Middleton 1, Barbara Marebwa 1, Chris Rorden 2, Julius Fridriksson 3, Stacy Fritz 1 and Troy Herter 1 Departments of Exercise Science 1, Psychology 2 and Communication Sciences and Disorders 3, University of South Carolina Approximately one million Americans live with chronic post-stroke disabilities, including limitations performing daily activities. Many of these daily activities, such as driving, involve continuous and simultaneous interactions between perceptual, cognitive, and motor systems. However, we have a limited understanding of the dynamic interactions between brain regions involved in performing these cognitive sensorimotor skills. Objective: Here we investigate the extent to which connectivity within and between three neural networks (sensorimotor, language, and prefrontal- basal ganglia) can predict performance on the Trail Making Test (TMT), a neuropsychological test predictive of post-stroke driving performance 1,3. Introduction Group# SubjectsAge RangeGender (Male/Female)Handedness (R/L/A) Stroke38(61) /1329/9/0 Participants Methods Subjects sat in a modified wheelchair base and grasped two handles linked to robotic motors. Visual targets were projected onto a screen on the same plane as the hands. Subjects moved the handles to perform the TMT. In TMT-A, participants moved their hands in ascending numerical order between numbered targets (1, 2, 3, …). In TMT-B, participants moved their hands between lettered and numbered targets (1, A, 2, B, 3, …). Three quantitative measures of TMT performance were assessed: Total Time, Movement Time (time the hand was moving between targets) and Dwell Time (time the hand was at one of the targets). Neuroimaging & Connectivity Analysis Several imaging sequences were collected from each patient: T1-MRI, T2-MRI, resting state fMRI, FLAIR, DTI, and perfusion MRI. High-resolution maps of lesion locations generated with the nii_stat tools in SPM8. 2 MRIcron (Chris Rorden, Columbia, SC; was used to display lesion maps and examine regions with overlapping lesions. T1 images were normalized and scalp-stripped, then warped to tFA images for purposes of computing connectivity. Transformations used to reslice ROI map to diffusion space. Connectivity quantified using probabilistic tractography that was performed using FSL’s Bayesian Estimation of Diffusion Parameters with Sampling Techniques and Probtrackx2. 4,5 Connectivity was extracted from a connectivity matrix using in-house tools developed in Matlab. To account for individual variability between subjects, the bias term (connectivity in left hemisphere network – connectivity in right hemisphere network / (sum of left and right connectivity)) was computed for each subject Significant correlations between mean connectivity and TMT scores were computed using SPSS 6. Robotic Apparatus and Task Behavioral Results Conclusions References 1.Salthouse TA. What cognitive abilities are involved in trail-making performance? Intelligence, 39: , Rorden C, Bonilha L, Fridriksson J, Bender B, Karnath HO (2012) Age-specific CT and MRI templates for spatial normalization. Neuroimage 61: Arbuthnott K, Frank J Trail Making Test, Part B as a Measure of Executive Control: Validation Using a Set-Switching Paradigm. J Clin Exp Neuropsych. 22:4: Behrens TEJ, Johansen B, Jbabdi S, Rushworth MFS, Woolrich MW Probabilistic diffusion tractography with multiply fiber orientations: what can we gain? Neuroimage 34(1): doi: /j.neuroimage M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith FSL. NeuroImage, 62: IBM Corp. Released IBM SPSS Statistics for Macintosh, Version Armonk, NY: IBM Corp. 7.Rorden C, Brett M Stereotaxic Display of Brain Lesions. Behav Neurol. 12(4): Reduced connectivity in the sensorimotor network is associated with decreases in visuomotor processing speed (increases in Total Time and Movement Time during TMT-A). Decreases in connectivity of the language network are associated with declines in visuomotor processing speed, indicating that the TMT may involve language processing. Connectivity of prefrontal basal ganglia network is not a significant predictor of TMT performance, suggesting limited involvement in some cognitive visuomotor skills. Low correlations between connectivity and performance in TMT-B may reflect the substantial inter-subject variability observed in TMT-B. Decreases in cortical connectivity may predict chronic activity limitations following stroke. Interventions designed to improve anatomical and functional connectivity of cortical regions may help improve post-stroke disability. Connectivity Results Lesion Overlap Sensorimotor NetworkLanguage NetworkCombined Network Time (sec) Behavioral Measure Mean TMT Times Total Times, Dwell Times and Movement times were significantly longer and more variable in TMT-B. Total Time In both TMT-A and TMT-B were significantly correlated to Dwell Time and Movement Time. r= p = Individual Network Connectivity Both the sensorimotor and language networks exhibited significant relationships (p<0.05) with performance on TMT-A. After correcting for multiple comparisons, the sensorimotor and language networks showed significant correlations with performance of TMT-A (p< ), but not TMT-B. There were no correlations between connectivity within the prefrontal basal ganglia network and TMT performance. None of three networks were significantly correlated with the time difference (B-A) or time ratio (B/A) of the TMT tasks. Combined Network Connectivity Total connectivity within and between sensorimotor and language networks was significantly correlated with Total Time, Dwell Time, and Movement Time in TMT-A (p< ). Within this larger network, we also observed marginal correlations with Total Time and Movement Time in TMT-B (p<0.05). Lesion Overlap Map Lesion overlap maps (above) showing average lesion locations Lesions overlapped in the middle cerebral artery territory (maps above and table on right) Total Time vs. Movement TimeTotal Time vs. Dwell Time Total Time Dwell TimeMovement Time r= p = r= p = r= p = r= p = r= p = r= p = r= p = r= p = r= p= r= p = r= p = 0.021