RESULTS AND DISCUSSION Support Vector Machine Approach for Prediction of Subclinical Language Deficit using Post-Stroke White Matter Tracts Rosaleena Mohanty, Svyatoslav Vergun, Pouria Mossahebi, Veena Nair, Vivek Prabhakaran University of Wisconsin – Madison BACKGROUND Group Shorthand LD+ LD- NC Clinically Aphasic No Subclinical LD Yes Sample Size (N) 10 Normed VF < -1.5 >= -1.5 Gender 3F/7M Mean Age 57.6±13.6 57.2±14.2 55.6±16.1 Handedness 8R/1L/1A 10 R 9R/1L RESULTS AND DISCUSSION Neuropsychological evaluation can take a long time to assess impairments following an event of stroke, especially among subjects who do not exhibit overt clinical deficits. Diffusion tensor imaging (DTI) is a non-invasive imaging modality and is helpful to characterize microstructural changes in tissue and injuries to white matter post-stroke. Machine learning classifier is a useful tool that helps distinguish between groups and elucidate features of distinction. Table below shows binary SVM performance. Groups NC vs LD+ LD+ vs LD- LD- vs NC LOOCV Accuracy 75% 80% 85% Sensitivity 70% Specificity 90% Cost C 10 1 0.1 Top Five Influential Features RD (R-external capsule) RD (R-uncinate fasciculus) MD (R-External Capsule) AD (L-inferior cerebral peduncle) AD (R-external capsule) RD (Fornix) AD (R-corticospinal tract) MD (R-medial lemniscus) RD (R-medial lemniscus) RD (R-cingulum) FA (L-inferior cerebral peduncle) AD (R-medial lemniscus) TR = 9000 ms; TE = 66.2 ms; single average (NEX = 1); field of view = 256  256 mm2; matrix size = 256  256; 75 axial slices with no gap between slices and slice thickness = 2 mm; excitation flip angle  = 90°; 56 gradient encoded directions, b value = 1000 s/mm2. Preprocessing: Using the DTI-TK plugin from FSL, maps for four DTI measures were generated, namely: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Individual 48 white matter tracts were extracted using Johns Hopkins University ICBM-DTI-81 white matter atlas depicted below. The mean value of each measure per tract per subject was computed. Data Analysis: A binary linear support vector machine (SVM) classifier was implemented pairwise on groups using the mean values of FA, MD, AD and RD as input features on MATLAB using Spider library. Using a leave-one-out cross validation (LOOCV), each time SVM helped identify specific DTI measures that were most influential in differentiating between groups with optimized classification cost C and number of top features. AIMS To understand involvement of specific white matter tracts in order to identify subclinical language deficit (LD) after stroke by comparing multiple DTI based measures across various groups with the help of a machine learning classifier. Features driving each of the three classifiers involve a weighted combination of all 4 DTI measures in specific tracts. However, a higher number of MD values appear among all the top features, followed by RD, AD and FA. Multiple DTI measures of the same tract (as in the table) among the top features shows the importance of the tract. Prior evidence shows the link between lesion in external capsule and forms of paraphasia which is in line with findings here.3 While the sample size per group is small, this shows that with a larger dataset, a classifier such as SVM has the potential to identify specific measures to differentiate between groups of subjects who do not show clinical deficits post stroke. Such a classifier could aid the neuropsychological assessment for early detection of subclinical impairments post stroke. METHODS Participants: A total of thirty subjects were categorized into three groups based on their stroke status and normed verbal fluency (VF) score obtained from neuropsychological test: stroke subjects with subclinical LD (LD+), stroke subjects without subclinical LD (LD-) and healthy normal controls (NC). Subjects with normed VF of less than -1.5 were termed to have subclinical LD. Subjects were age and gender matched across the groups. None of the subjects were clinically aphasic. The following table enlists demographic and clinical details of each of the three groups. Gender is denoted by F (female) or M (male), handedness is denoted by R (right), L (left) or A (ambidextrous). Imaging Data: DTI with 56 directions were collected on 3T GE MRI 750 scanners using single-shot echo planar imaging (EPI) with the following parameters: REFERENCES P. Mossahebi, et al. Reduced White Matter Integrity in Acute Stroke Patients With Language Deficits: a Tract-based Spatial Statistics Study. Stroke.2016;47:AWMP22. S. Vergun, et al. Characterizing functional connectivity differences in aging adults using machine learning on resting state fMRI data. Front. Comput. Neurosci. 2013;7:38. Kreisler A., et al. . (2000). The anatomy of aphasia revisited. Neurology 54, 1117–1123. 10.1212/WNL.54.5.1117