A unified neurocognitive model of the anterior temporal lobe contributions to semantics, language, social behaviour &amp; face recognition

Ding, Junhua; Chen, K.; Liu, H.; Huang, Lin; Chen, Y.; Lv, Yubao; Yang, Qing; Guo, Qihao; Han, Zaizhu; Ralph, Matthew A. Lambon

doi:10.1101/725515

Cited by 1 publication

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References 114 publications

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“…A related prodromal phase has been described for progressive supranuclear palsy (PSP) (Whitwell et al ., 2019; Josephs et al ., 2005; Josephs and Duffy, 2008; Peterson et al ., 2019). The development of frontal features of disinhibition and/or obsessionality following presentation with ‘pure’ svPPA is also a common clinical sequence (Ding et al ., 2020; Murley et al ., 2020).…”

Section: Discussionmentioning

confidence: 99%

The Mini Linguistic State Examination (MLSE): a brief but accurate assessment tool for classifying Primary Progressive Aphasias

Patel

Peterson

Ingram

et al. 2020

Preprint

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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.

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