Journal article
Simulated attack reveals how lesions affect network properties in post-stroke aphasia
The Journal of neuroscience, pJN-RM-1163-21
11 May 2022
PMID: 35545436
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomical network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomical network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores (the Western Aphasia Battery Aphasia Quotient; WAB-AQ, four behavioral factor scores obtained from a neuropsychological battery, and a factor sum), we compared the behavioral variance accounted for by simulated post-stroke connectomes to that observed in the randomly permuted data. Global measures of anatomical network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.
Post-stroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often utilize small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. "Simulated attack" models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomical networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.
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Details
- Title
- Simulated attack reveals how lesions affect network properties in post-stroke aphasia
- Creators
- John D Medaglia - Drexel UniversityBrian A Erickson - Drexel UniversityDorian Pustina - CHDI FoundationApoorva S Kelkar - Drexel UniversityAndrew T DeMarco - Georgetown UniversityJ Vivian Dickens - Georgetown UniversityPeter E Turkeltaub - Georgetown University
- Publication Details
- The Journal of neuroscience, pJN-RM-1163-21
- Publisher
- Society for Neuroscience
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000817190500003
- Scopus ID
- 2-s2.0-85132453196
- Other Identifier
- 991019168197204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Neurosciences