Poster B5, Tuesday, August 20, 2019, 3:15 – 5:00 pm, Restaurant Hall
Using Vascular Territories to Estimate Disconnection Profiles in Post-Stroke Aphasia
Natalie Busby1, Ajay Halai1,2, Ying Zhao2, Geoff Parker3,4, Matt Lambon Ralph1,2;1Division of Neuroscience and Experimental Psychology, University of Manchester, 2MRC Cognition and Brain Sciences Unit, University of Cambridge, 3Centre of Medical Image Computing, UCL, 4Bioxydyn Ltd.
White matter disconnection is important for understanding disorders of higher-order functions such as language, however as diffusion data is rarely collected clinically, recent studies have attempted to predict disconnection patterns using other factors. Lesions are often overlaid onto white matter atlases to estimate disconnection however, although damage sustained to the brain post-stroke appears random, it is actually constrained by the underlying neurovasculature; brain regions supplied by the occluded arterial branch will be affected. Therefore, accounting for this by investigating which vascular territories were damaged may yield an interesting way to predict disconnection. Consequently, the aims of this study were; (a)to identify disconnection profiles associated with each vascular territory of the middle cerebral artery (MCA), (b)to determine whether territories can be combined to ‘build’ a lesion, and (c)to predict disconnection patterns in post-stroke aphasia patients by the summing damage associated with each vascular territory used to ‘build’ their lesion. Anatomical Connectivity Mapping (ACM) assesses long-range disconnection and may be a complementary alternative to local connectivity measures. Using probabilistic tractography, ACMs are obtained by initiating streamlines from all voxels. Cumulative trajectories of streamlines are saved, providing a brain map indicating how many times a streamline passed each voxel (i.e. the global connectivity of each voxel). This identifies widespread disconnection as fewer streamlines would pass through any voxel connected to damaged regions and may enable the prediction of disconnection profiles accounting for damage away from the lesion. This also allows for ‘pseudo-lesioning’; the selective removal of each vascular territory from healthy controls. Disconnection profiles can then be calculated for each territory in every individual. Vascular territories were combined to match lesions in 62 individuals with aphasia following a left hemispheric MCA stroke. ACM was used to estimate disconnection in each individual using which territories best matched their lesion. This was compared back to real patient connectivity. On average, 4.58 vascular territories were combined to ‘build’ the lesion. High similarity scores were found between the lesion and combined territories. There was a significant positive correlation between lesion volume and similarity scores(r=0.665,p<0.001). High similarity scores were found between actual and predicted patient connectivity. A better match between the territories and the lesion positively correlated with a more accurate prediction of disconnection. Individuals with lesions smaller than 10,000 voxels had a significantly less accurate prediction of connectivity than larger lesions(t(62)=7.72,p<0.001). Selectively removing each vascular territory revealed disconnection associated with damage to each territory. Strikingly, disconnection extended far beyond the removed region. The high similarity between each lesion and combined territories suggests the underlying neurovasculature can explain damage sustained following a left hemispheric MCA stroke resulting in aphasia. The high similarity scores between predicted and real patient connectivity scores suggests that connectivity can be predicted using the underlying neurovasculature, particularly for larger lesions. This novel methodology demonstrated that disconnection following a left-hemispheric stroke can be explained by the underlying neurovasculature of the MCA. This may enable a better understanding of language deficits where there is no scope for the collection of diffusion data in the patients themselves.
Themes: Disorders: Acquired, Methods
Method: White Matter Imaging (dMRI, DSI, DKI)