This study presents the results of an analysis of pragmatic aspects of language samples obtained from five agrammatic aphasic individuals prior to and following Linguistic Specific Treatment (LST). Clinically and statistically significant positive changes in informativeness and efficiency were documented in posttreatment samples. Analysis of communicative competence (i.e., naïve listeners' ratings of pre- and posttreatment audiotaped samples), was undertaken to examine the social validity of quantified changes. Mean ratings across listeners were computed to evaluate their subjective perceptions of general communicative constructs. Results indicated that objectively measured changes in pragmatic aspects were perceptible to naïve listeners, however, to varying degrees across participants and constructs.

For two decades, researchers and clinicians have been using the diagnostic criteria for FTD to generally diagnose a patient as suffering from PPA and the criteria of Neary et al. (1998) to further specify the diagnosis as progressive nonfluent aphasia or semantic dementia. However, there were a number of PPA cases that could not be classified according to the criteria of Neary and colleagues, which led to a revision of the diagnostic clinical and research criteria for PPA by Gorno-Tempini et al. (2011). The revised criteria encompass three PPA variants (svPPA, nfvPPA, and lvPPA) with three stages characterized by increasing evidence: clinical diagnosis, imaging-supported diagnosis, and diagnosis with definite pathology. As compared to the previous diagnostic criteria, more emphasis is placed on imaging markers as supportive features. These imaging criteria were however proposed based on a purely qualitative evaluation of the literature and have not been validated so far. The aim of this thesis was to quantitatively evaluate the validity of the new diagnostic imaging criteria for PPA variants using anatomical likelihood meta-analyses (study 1) and to investigate the usefulness of these imaging criteria for the individual diagnosis of PPA patients in clinical routine using support vector machine classification (study 2).