Spanish Version of the Mini-Linguistic State Examination for the Diagnosis of Primary Progressive Aphasia

Primary progressive aphasia: six questions in search of an answer

Here, we review recent progress in the diagnosis and management of primary progressive aphasia-the language-led dementias. We pose six key unanswered questions that challenge current assumptions and highlight the unresolved difficulties that surround these diseases. How many syndromes of primary progressive aphasia are there-and is syndromic diagnosis even useful? Are these truly 'language-led' dementias? How can we diagnose (and track) primary progressive aphasia better? Can brain pathology be predicted in these diseases? What is their core pathophysiology? In addition, how can primary progressive aphasia best be treated? We propose that pathophysiological mechanisms linking proteinopathies to phenotypes may help resolve the clinical complexity of primary progressive aphasia, and may suggest novel diagnostic tools and markers and guide the deployment of effective therapies.

The neural substrates of transdiagnostic cognitive-linguistic heterogeneity in primary progressive aphasia

BACKGROUND

Clinical variants of primary progressive aphasia (PPA) are diagnosed based on characteristic patterns of language deficits, supported by corresponding neural changes on brain imaging. However, there is (i) considerable phenotypic variability within and between each diagnostic category with partially overlapping profiles of language performance between variants and (ii) accompanying non-linguistic cognitive impairments that may be independent of aphasia magnitude and disease severity. The neurobiological basis of this cognitive-linguistic heterogeneity remains unclear. Understanding the relationship between these variables would improve PPA clinical/research characterisation and strengthen clinical trial and symptomatic treatment design. We address these knowledge gaps using a data-driven transdiagnostic approach to chart cognitive-linguistic differences and their associations with grey/white matter degeneration across multiple PPA variants.

METHODS

Forty-seven patients (13 semantic, 15 non-fluent, and 19 logopenic variant PPA) underwent assessment of general cognition, errors on language performance, and structural and diffusion magnetic resonance imaging to index whole-brain grey and white matter changes. Behavioural data were entered into varimax-rotated principal component analyses to derive orthogonal dimensions explaining the majority of cognitive variance. To uncover neural correlates of cognitive heterogeneity, derived components were used as covariates in neuroimaging analyses of grey matter (voxel-based morphometry) and white matter (network-based statistics of structural connectomes).

RESULTS

Four behavioural components emerged: general cognition, semantic memory, working memory, and motor speech/phonology. Performance patterns on the latter three principal components were in keeping with each variant's characteristic profile, but with a spectrum rather than categorical distribution across the cohort. General cognitive changes were most marked in logopenic variant PPA. Regardless of clinical diagnosis, general cognitive impairment was associated with inferior/posterior parietal grey/white matter involvement, semantic memory deficits with bilateral anterior temporal grey/white matter changes, working memory impairment with temporoparietal and frontostriatal grey/white matter involvement, and motor speech/phonology deficits with inferior/middle frontal grey matter alterations.

CONCLUSIONS

Cognitive-linguistic heterogeneity in PPA closely relates to individual-level variations on multiple behavioural dimensions and grey/white matter degeneration of regions within and beyond the language network. We further show that employment of transdiagnostic approaches may help to understand clinical symptom boundaries and reveal clinical and neural profiles that are shared across categorically defined variants of PPA.

Early Identification of Different Behavioral Phenotypes in the Behavioral Variant of Frontotemporal Dementia with the Aid of the Mini-Frontal Behavioral Inventory (mini-FBI)

Background: The Frontal Behavioral Inventory (FBI) is a questionnaire designed to quantify behavioral changes in frontotemporal dementia (FTD). Literature showed heterogeneous FBI profiles in FTD versus Alzheimer’s disease (AD) with variable occurrence of positive and negative symptoms. Objective: In this study, we constructed a short FBI version (i.e., mini-FBI) with the aim to provide clinicians with a short tool for the identification of early behavioral changes in behavioral variant of FTD (bvFTD), also facilitating the differential diagnosis with AD. Methods: 40 bvFTD and 33 AD patients were enrolled. FBI items were selected based on internal consistency and exploratory factor analysis. Convergent validity of mini-FBI was also assessed. A behavioral index (i.e., B-index) representing the balance between positive and negative mini-FBI symptoms was computed in order to analyze its distribution in bvFTD through a cluster analysis and to compare performance among patient groups. Results: The final version of the mini-FBI included 12 items, showing a significant convergent validity with the Neuropsychiatric Inventory scores (rp = 0.61, p < 0.001). Cluster analysis split patients in four clusters. bvFTD were included in three different clusters characterized by prevalent positive symptoms, both positive and negative symptoms, or prevalent negative behavioral alterations, similar to a subset of AD patients. A fourth cluster included only AD patients showing no positive symptoms. Conclusion: The mini-FBI is a valuable easily administrable questionnaire able to early identify symptoms effectively contributing to the bvFTD behavioral syndrome, aiding clinician in diagnosis and management.

Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study

Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.