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Hemodynamic response function in patients with stroke-induced aphasia: Implications for fMRI data analysis., , and . NeuroImage, 36 (2): 322-331 (2007)Neuroimaging in aphasia treatment research: Standards for establishing the effects of treatment., , , , , , , and . NeuroImage, (2013)A machine learning approach for predicting post-stroke aphasia recovery: a pilot study., , , , , , , , , and 3 other author(s). PETRA, page 22:1-22:9. ACM, (2020)Corrigendum to "Neuroimaging in aphasia treatment research: Consensus and practical guidelines for data analysis" Neuroimage 73 (2013) 215-224., , , , , , , , and . NeuroImage, (2013)Network modulation during complex syntactic processing., , , , , , , and . NeuroImage, 59 (1): 815-823 (2012)The resolution and recovery of filler-gap dependencies in aphasia: Evidence from on-line anomaly detection, and . (2004)Neuroimaging in aphasia treatment research: Issues of experimental design for relating cognitive to neural changes., , , , and . NeuroImage, (2013)Using statistical parsing to detect agrammatic aphasia., , , , and . BioNLP@ACL, page 134-142. Association for Computational Linguistics, (2014)Detecting linguistic HCI markers in an online aphasia support group., , , and . ASSETS, page 65-70. ACM, (2012)Neuroimaging in aphasia treatment research: Quantifying brain lesions after stroke., , , , and . NeuroImage, (2013)