Leonardo Bonilha1, Chris Rorden, Julius Fridriksson. 1. From the Department of Neurology, Medical University of South Carolina, Charleston (L.B.); and Departments of Psychology (C.R.) and Communication Sciences and Disorders (J.F.), University of South Carolina, Columbia.
Abstract
BACKGROUND AND PURPOSE: Studies assessing the relationship between chronic poststroke language impairment (aphasia) and ischemic brain damage usually rely on measuring the extent of brain necrosis observed on MRI. Nonetheless, clinical observation suggests that patients can exhibit deficits that are more severe than what would be expected based on lesion location and size. This phenomenon is commonly explained as being the result of cortical disconnection. To understand whether disconnection contributes to clinical symptoms, we assessed the relationship between language impairments and structural brain connectivity (the connectome) in patients with chronic aphasia after a stroke. METHODS: Thirty-nine patients with chronic aphasia underwent language assessment and MRI scanning. Relying on MRI data, we reconstructed the individual connectome from T1-weighted and diffusion tensor imaging. Deterministic fiber tractography was used to assess connectivity between each possible pair of cortical Brodmann areas. Multiple linear regression analyses were performed to evaluate the relationship between language performance and cortical necrosis and cortical disconnection. RESULTS: We observed that structural disconnection of Brodmann area 45 (spared by the necrotic tissue) was independently associated with naming performance, controlling for the extent of Brodmann area 45 necrosis (F=4.62; P<0.01; necrosis: β=0.43; P=0.03; disconnection β=1.21; P<0.001). CONCLUSIONS: We suggest that cortical disconnection, as measured by the structural connectome, is an independent predictor of naming impairment in patients with chronic aphasia. The full extent of clinically relevant brain damage after an ischemic stroke may be underappreciated by visual inspection of cortical necrosis alone.
BACKGROUND AND PURPOSE: Studies assessing the relationship between chronic poststroke language impairment (aphasia) and ischemic brain damage usually rely on measuring the extent of brain necrosis observed on MRI. Nonetheless, clinical observation suggests that patients can exhibit deficits that are more severe than what would be expected based on lesion location and size. This phenomenon is commonly explained as being the result of cortical disconnection. To understand whether disconnection contributes to clinical symptoms, we assessed the relationship between language impairments and structural brain connectivity (the connectome) in patients with chronic aphasia after a stroke. METHODS: Thirty-nine patients with chronic aphasia underwent language assessment and MRI scanning. Relying on MRI data, we reconstructed the individual connectome from T1-weighted and diffusion tensor imaging. Deterministic fiber tractography was used to assess connectivity between each possible pair of cortical Brodmann areas. Multiple linear regression analyses were performed to evaluate the relationship between language performance and cortical necrosis and cortical disconnection. RESULTS: We observed that structural disconnection of Brodmann area 45 (spared by the necrotic tissue) was independently associated with naming performance, controlling for the extent of Brodmann area 45 necrosis (F=4.62; P<0.01; necrosis: β=0.43; P=0.03; disconnection β=1.21; P<0.001). CONCLUSIONS: We suggest that cortical disconnection, as measured by the structural connectome, is an independent predictor of naming impairment in patients with chronic aphasia. The full extent of clinically relevant brain damage after an ischemic stroke may be underappreciated by visual inspection of cortical necrosis alone.
Authors: D Hervé; N Molko; S Pappata; F Buffon; D LeBihan; M-G Bousser; H Chabriat Journal: J Neurol Neurosurg Psychiatry Date: 2005-02 Impact factor: 10.154
Authors: Grant M Walker; Myrna F Schwartz; Daniel Y Kimberg; Olufunsho Faseyitan; Adelyn Brecher; Gary S Dell; H Branch Coslett Journal: Brain Lang Date: 2010-10-18 Impact factor: 2.381
Authors: Fernanda Tovar-Moll; Iordanis E Evangelou; Annie W Chiu; Sungyoung Auh; Christina Chen; Mary Ehrmantraut; Joan M Ohayon; Nancy Richert; Francesca Bagnato Journal: J Neuroimaging Date: 2014-10-16 Impact factor: 2.486
Authors: Julius Fridriksson; Dirk-Bart den Ouden; Argye E Hillis; Gregory Hickok; Chris Rorden; Alexandra Basilakos; Grigori Yourganov; Leonardo Bonilha Journal: Brain Date: 2018-03-01 Impact factor: 13.501
Authors: Adrian G Guggisberg; Philipp J Koch; Friedhelm C Hummel; Cathrin M Buetefisch Journal: Clin Neurophysiol Date: 2019-04-15 Impact factor: 3.708