Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

Erickson, B. A.; Kim, B.; Deck, B.; Pustina, D.; DeMarco, A. T.; Dickens, J. V.; Kelkar, A.; Turkeltaub, P.; Medaglia, J.

doi:10.1101/2021.08.31.458462

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bioRxiv · 10.1101/2021.08.31.458462

Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

Erickson, B. A.·Kim, B.·Deck, B.·Pustina, D.·DeMarco, A. T.·Dickens, J. V.·Kelkar, A.·Turkeltaub, P.·Medaglia, J.

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Abstract

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain. Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than those observed in the average network of the most reliably observed connections in age-matched controls. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a factor analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using grid-search cross-validated Support Vector Regression, a technique that extracts robust relationships in high-dimensional data analysis. We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

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Erickson, B. A., Kim, B., Deck, B., Pustina, D., DeMarco, A. T., Dickens, J. V., Kelkar, A., Turkeltaub, P., Medaglia, J.. 2021-09-02. Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke. https://doi.org/10.1101/2021.08.31.458462

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