A ‘Mini Linguistic State Examination’ to classify primary progressive aphasia

Abstract

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.

Prevalence and determinants of language impairment in non‐demented amyotrophic lateral sclerosis patients

“…Notably, this last notion would be supported by the finding that ECAS‐Executive scores strongly predict the ECAS‐Language. It is thus advisable that, when screening for LI in non‐demented ALS patients, the ECAS be complemented with further, domain‐specific measures such as the Screening for Aphasia in NeuroDegeneration [ 31 ] or the Mini‐Linguistic State Examination [ 32 ], which have both been developed in order to deliver a first‐level estimate of global language functioning by focusing on the semiology of LI typical of neurodegenerative conditions.…”

Neuroanatomical correlates of screening for aphasia in NeuroDegeneration (SAND) battery in non-fluent/agrammatic variant of primary progressive aphasia

“…Moreover, these tests may sometimes lack sensitivity to detect the specific deficits present in PPA (Savage et al, 2013 ). To overcome this problem, recently, some authors developed new batteries, with the aim of harmonizing the diagnostic process between different centers (Savage et al, 2013 ; Catricalà et al, 2017 ; Epelbaum et al, 2020 ; Patel et al, 2022 ). In particular, Catricalà et al ( 2017 ) implemented a new screening battery, the Screening of Aphasia for NeuroDegneration (SAND), capable of capturing the key language features required for the diagnosis and classification of PPAs through the assessment of different components of language.…”

“…The battery incorporates a set of tests adapted to measure specific linguistic domains in PPA, including assessments of lexical retrieval, syntax and semantic processes. Moreover, it proposes a performance classification based on the quantitative and qualitative error analysis to better identify the nature of the language dysfunctions related to a specific variant of PPA (Catricalà et al, 2017 ; Battista et al, 2018 ; Patel et al, 2022 ). Since this brief battery was developed taking into account the psycholinguistic factors that can affect PPA patients' performance, it might represent a valuable screening tool for detecting language impairment in neurodegenerative disorders (Catricalà et al, 2017 ; Battista et al, 2018 ).…”

Primary progressive aphasia: in search of brief cognitive assessments

“…First, Patel et al . 3 describe the Mini Linguistic State Examination (MLSE) and present the first validation study of the MLSE in a cohort of 54 patients with PPA. The MLSE is a brief test that consists of 11 subtests focused on speech and language.…”

“… 5 provided an automated calculator, and Patel et al . 3 trained machine learning algorithms and proposed a decision tree for guiding PPA diagnosis. Machine learning techniques may be a promising method in the identification of subgroups, by predicting diagnoses using test scores and selecting the most sensitive test items, thereby reducing the length of evaluations and ultimately moving towards computer-aided diagnosis.…”