Patients with diverse neurocognitive conditions frequently exhibit measurable language deficits that serve as biomarkers for differential diagnosis and therapy decision making. Discourse analysis can offer reliable ecological measures of human communication, yet manual discourse analysis is cumbersome. Recent advances in automated analysis software provide quick and easy extraction of raw language metrics in the clinic. Nevertheless, transforming these measures into actionable clinical insights remains a significant challenge. The aim of this paper is to present the Automated Language Biomarker Application (ALBA), an integrated framework developed within the Open Brain AI ecosystem to bridge the gap between feature extraction and clinical interpretation. ALBA provides clinicians with a robust statistical infrastructure to benchmark individual patient measures against standardized, large-scale clinical corpora. By utilizing a shared elicitation and processing pipeline, the application ensures that user-provided data are directly comparable to population norms for conditions including Aphasia, Mild Cognitive Impairment (MCI), Dementia, and other neurological conditions. The system implements adaptive statistical logic, employing one-sample t-tests and robust non-parametric alternatives to provide real-time significance testing and dynamic visualizations (box, bar, and violin plots). By automating the comparison of “Language Signatures” against healthy controls and specific clinical phenotypes, ALBA facilitates rapid, evidence-based decision-making in both research and rehabilitation contexts.

This paper evaluates global and local semantic coherence in aphasic and non-aphasic discourse tasks using the Tool for the Automatic Analysis of Cohesion (TAACO). The motivation for this paper stems from a lack of automatic methods to evaluate discourse-level phenomena, such as semantic cohesion, in transcripts derived from persons with aphasia. It leverages existing test-retest data to evaluate two main objectives: (1) Test-Retest Reliability, to identify if variables significantly differ across test and retest time points for either group (aphasia, control), and (2) Inter-Group Discourse Cohesion, where aphasic discourse is expected to be less cohesive than control discourse, resulting in lower cohesion scores for the aphasia group. Exploratory analysis examines correlations between variables for both groups, identifying any relationships between word-level and sentence-level semantic variables. Results verify that semantic cohesion and coherence are generally preserved in both groups, except for word-level and a few sentence-level semantic measures,w which are higher for the control group. Overall, variables tend to be reliable across time points for both groups. Notably, the aphasia group demonstrates more variability in cohesion than the control group, which is to be expected after brain injury. A close relationship between word-level indices and other indices is observed, suggesting a disconnection between word-level factors and sentence-level metrics.