Neuro-Clinical Signatures of Language Impairments after Acute Stroke: A VBQ Analysis of Quantitative Native CT Scans.

Ischemic stroke affects language production and/or comprehension and leads to devastating long-term consequences for patients and their families. Previous studies have shown that neuroimaging can increase our knowledge of the basic mechanisms of language recovery. Currently, models for predicting patients' outcomes have limited use in the clinic for the evaluation and optimization of rehabilitative strategies mostly because that are often based on high-resolution magnetic resonance imaging (MRI) data, which are not always possible to carry out in the clinical routine. Here, we investigate the use of Voxel-Based Morphometry (VBM), multivariate modelling and native Computed Tomography (nCT) scans routinely acquired in the acute stage of stroke for identifying biological signatures that explicate the relationships between brain anatomy and types of impairments. 80 stroke patients and 30 controls were included. nCT-scans were acquired in the acute ischemia stage and bedside clinical assessment from board-certified neurologist based on the NIH stroke scale. We use a multivariate Principal Component Analyses (PCA) to identify the brain signatures group the patients according to the presence or absence of impairment and identify the association between local Grey Matter (GM) and White Matter (WM) nCT values with the presence or absence of the impairment. Individual patient's nCT scans were compared to a group of controls' with no radiological signs of stroke to provide an automated delineation of the lesion. Consistently across the whole group the regions that presented significant difference GM and WM values overlap with known areas that support language processing. In summary, the method applied to nCT scans performed in the acute stage of stroke provided robust and accurate information about brain lesions' location and size, as well as quantitative values. We found that nCT and VBQ analyses are effective for identifying neural signatures of concomitant language impairments at the individual level, and neuroanatomical maps of aphasia at the population level. The signatures explicate the neurophysiological mechanisms underlying aetiology of the stroke. Ultimately, similar analyses with larger cohorts could lead to a more integrated multimodal model of behaviour and brain anatomy in the early stage of ischemic stroke.

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