Language impairments caused by stroke (post-stroke aphasia, PSA) and neurodegeneration (primary progressive aphasia, PPA) have overlapping symptomatology, nomenclature and are classically divided into categorical subtypes. Surprisingly, PPA and PSA have rarely been directly compared in detail. Rather, previous studies have compared certain subtypes (e.g. semantic variants) or have focused on a specific cognitive/linguistic task (e.g. reading). This study assessed a large range of linguistic and cognitive tasks across the full spectra of PSA and PPA. We applied varimax-rotated principal component analysis to explore the underlying structure of the variance in the assessment scores. Similar phonological, semantic and fluency-related components were found for PSA and PPA. A combined principal component analysis across the two aetiologies revealed graded intra- and intergroup variations on all four extracted components. Classification analysis was used to test, formally, whether there were any categorical boundaries for any subtypes of PPA or PSA. Semantic dementia formed a true diagnostic category (i.e. within group homogeneity and distinct between-group differences), whereas there was considerable overlap and graded variations within and between other subtypes of PPA and PSA. These results suggest that (i) a multidimensional rather than categorical classification system may be a better conceptualization of aphasia from both causes; and (ii) despite the very different types of pathology, these broad classes of aphasia have considerable features in common.

There are few available methods for qualitatively evaluating patients with primary progressive aphasia. Commonly adopted approaches are time-consuming, of limited accuracy, or designed to assess different patient populations. This paper introduces a new clinical test - the Mini Linguistic State Examination - which was designed uniquely to enable a clinician to assess and subclassify both classical and mixed presentations of primary progressive aphasia. The adoption of a novel assessment method (error classification) greatly amplifies the clinical information that can be derived from a set of standard linguistic tasks and allows a five-dimensional profile to be defined. Fifty-four patients and 30 matched controls were recruited. Five domains of language competence (motor speech, phonology, semantics, syntax, and working memory) were assessed using a sequence of 11 distinct linguistic assays. A random forest classification was used to assess the diagnostic accuracy for predicting primary progressive aphasia subtypes and create a decision tree as a guide to clinical classification. The random forest prediction model was 96% accurate overall (92% for the logopenic variant, 93% for the semantic variant, and 98% for the non-fluent variant). The derived decision tree produced a correct classification of 91% of participants whose data were not included in the training set. The Mini Linguistic State Examination is a new cognitive test incorporating a novel and powerful, yet straightforward, approach to scoring. Rigorous assessment of its diagnostic accuracy confirmed excellent matching of primary progressive aphasia syndromes to clinical gold standard diagnoses. Adoption of the Mini Linguistic State Examination by clinicians will have a decisive impact on the consistency and uniformity with which patients can be described clinically. It will also facilitate screening for cohort-based research, including future therapeutic trials, and is suitable for describing, quantifying and monitoring language deficits in other brain disorders.

Background: This paper introduces a new clinical test, the Mini Linguistic State Examination (MLSE), as a short assessment for screening and classification of the different manifestations of primary progressive aphasia (PPA). Differentiation and monitoring of PPA variants are vital for management, planning and development of new treatments. The MLSE is designed to improve the uniformity of testing, screening for recruitment to clinical trials, and consistency of research results. It is a brief but effective test which can be adapted to the worlds major languages. Methods: Fifty-four patients and 30 age-, sex- and education-matched controls completed testing with the MLSE and components of the Boston Diagnostic Aphasia Examination in addition to their standard clinical diagnostic assessment. The MLSE includes five domains (motor speech, phonology, semantics, syntax and working memory) that were compared across groups. A random forest classification was used to learn the relationship between these five domains and assess the power of the diagnostic accuracy for predicting PPA subtypes. The final machine learning model was used to create a decision tree to guide the optimal manual classification of patients. Results: On average, the test took less than 20 minutes to administer. Significant group differences were found across all five domains, in terms of the distributions of error-types. These differences mirror the well-known language profiles for the three main PPA variants, which typically require an extended neuropsychology and speech pathology assessment. The random forest prediction model had an overall classification accuracy of 96% (92% for logopenic variant PPA, 93% for semantic variant PPA and 98% for non-fluent variant PPA). The derived decision tree for manual classification produced correct classification of 91% of participants whose data were not included in the training set. Conclusions: The MLSE is a new short cognitive test, with a scoring system that is easy to learn and apply. It is accurate for classifying PPA syndromes, and has potential to screen and monitor language deficits that occur in other focal and neurodegenerative brain disorders associated with language impairment. With increasing importance of language assessment in clinical research, the MLSEs linguistic assessment tool enables the essential profiling of language deficits in a wide clinical community.

Language impairments caused by stroke (post-stroke aphasia) and neurodegeneration (primary progressive aphasia) have overlapping symptomatology, nomenclature and are classically divided into categorical subtypes. Surprisingly, primary progressive aphasia (PPA) and post-stroke aphasia (PSA) have rarely been directly compared in detail. Rather previous studies have compared certain subtypes (e.g., semantic variants) or have focussed on a specific cognitive/linguistic task (e.g., reading). This study assessed a large range of linguistic and cognitive tasks across the full spectra of PSA and PPA. We applied varimax-rotated principal component analysis to explore the underlying structure of the variance in the assessment scores. Similar phonological, semantic and fluencyrelated components were found for PSA and PPA. A combined principal component analysis across the two aetiologies revealed graded intragroup and intergroup variations on all four extracted components. Classification analysis was employed to test, formally, whether there were any categorical boundaries for any subtypes of PPA or PSA. Semantic dementia proved to form a true diagnostic category (i.e., within group homogeneity and distinct between group differences), whereas there was considerable overlap and graded variations within and between other subtypes of PPA and PSA. These results suggest that (a) a multi-dimensional rather than categorical classification system may be a better conceptualisation of aphasia from both causes, and (b) despite the very different types of pathology, these broad classes of aphasia have considerable features in common.

Background: Progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) affect speech and language as well as motor functions. Clinical and neuropathological data indicate a close relationship between these two disorders and the non-fluent variant of primary progressive aphasia (nfvPPA). We use the recently developed Mini Linguistic State Examination tool (MLSE) to study speech and language disorders in patients with PSP, CBS, and nfvPPA, in combination with structural magnetic resonance imaging (MRI).Methods: Fifty-one patients (PSP N = 13, CBS N = 19, nfvPPA N = 19) and 30 age-matched controls completed the MLSE, the short form of the Boston Diagnostic Aphasia Examination (BDAE), and the Addenbrooke’s Cognitive Examination III. Thirty-eight patients and all controls underwent structural MRI at 3 Tesla, with T1 and T2-weighted images processed by surface-based and subcortical segmentation within FreeSurfer 6.0.0 to extract cortical thickness and subcortical volumes. Morphometric differences were compared between groups and correlated with the severity of speech and language impairment.Results: CBS and PSP patients showed impaired MLSE performance, compared to controls, with a similar language profile to nfvPPA, albeit less severe. All patient groups showed reduced cortical thickness in bilateral frontal regions and striatal volume. PSP and nfvPPA patients also showed reduced superior temporal cortical thickness, with additional thalamic and amygdalo-hippocampal volume reductions in nfvPPA. Multivariate analysis of brain-wide cortical thickness and subcortical volumes with MLSE domain scores revealed associations between performance on multiple speech and language domains with atrophy of left-lateralised fronto-temporal cortex, amygdala, hippocampus, putamen, and caudate.Conclusions: The effect of PSP and CBS on speech and language overlaps with nfvPPA. These three disorders cause a common anatomical pattern of atrophy in the left frontotemporal language network and striatum. The MLSE is a short clinical screening tool that can identify the language disorder of PSP and CBS, facilitating clinical management and patient access to future clinical trials.