Dataset Information

Using narratives in differential diagnosis of neurodegenerative syndromes.

ABSTRACT:

Purpose

Language decline has been associated with healthy aging and with various neurodegenerative conditions, making it challenging to differentiate among these conditions. This study examined the utility of linguistic measures derived from a short narrative language sample for 1) identifying language characteristics and cut-off scores to differentiate between healthy aging, Primary Progressive Aphasia (PPA), Mild Cognitive Impairment (MCI), and Alzheimer's dementia (AD); and 2) differentiating among PPA variants in which language is the primary impairment.

Method

Participants were 25 neurologically healthy English speakers, 20 individuals with MCI, 20 with AD, and 26 with PPA (non-fluent/agrammatic N = 10, logopenic N = 9, semantic N = 7). Narrative language samples of the Cookie Theft Picture of persons with healthy aging, MCI and AD were retrospectively obtained from the DementiaBank database (https://talkbank.org/DementiaBank/) and PPA samples were obtained from an ongoing research study. The language samples were analyzed for fluency, word retrieval success, grammatical accuracy, and errors using automated and manual analysis methods. The sensitivity and specificity of various language measures was computed.

Results

Participants with PPA scored lower than neurologically healthy and MCI groups on fluency (words per minute and disfluencies), word retrieval (Correct Information Units and number of errors), and sentence grammaticality. PPA and AD groups did not differ on language measures. Agrammatic PPA participants scored lower than logopenic and semantic PPA groups on several measures, while logopenic and semantic PPA did not differ on any measures.

Conclusion

Measures derived from brief language samples and analyzed using mostly automated methods are clinically useful in differentiating PPA from healthy aging and MCI, and agrammatic PPA from other variants. The sensitivity and specificity of these measures is modest and can be improved when coupled with clinical presentation.

SUBMITTER: Faroqi-Shah Y

PROVIDER: S-EPMC7304645 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:See Vidailhet et al. (doi:10.1093/brain/awx140) for a scientific commentary on this article. Misdiagnosis among tremor syndromes is common, and can impact on both clinical care and research. To date no validated neurophysiological technique is available that has proven to have good classification performance, and the diagnostic gold standard is the clinical evaluation made by a movement disorders expert. We present a robust new neurophysiological measure, the tremor stability index, which can discriminate Parkinson’s disease tremor and essential tremor with high diagnostic accuracy. The tremor stability index is derived from kinematic measurements of tremulous activity. It was assessed in a test cohort comprising 16 rest tremor recordings in tremor-dominant Parkinson’s disease and 20 postural tremor recordings in essential tremor, and validated on a second, independent cohort comprising a further 55 tremulous Parkinson’s disease and essential tremor recordings. Clinical diagnosis was used as gold standard. One hundred seconds of tremor recording were selected for analysis in each patient. The classification accuracy of the new index was assessed by binary logistic regression and by receiver operating characteristic analysis. The diagnostic performance was examined by calculating the sensitivity, specificity, accuracy, likelihood ratio positive, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-validation. Tremor stability index with a cut-off of 1.05 gave good classification performance for Parkinson’s disease tremor and essential tremor, in both test and validation datasets. Tremor stability index maximum sensitivity, specificity and accuracy were 95%, 95% and 92%, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.916 (95% confidence interval 0.797–1.000) for the test dataset and a value of 0.855 (95% confidence interval 0.754–0.957) for the validation dataset. Classification accuracy proved independent of recording device and posture. The tremor stability index can aid in the differential diagnosis of the two most common tremor types. It has a high diagnostic accuracy, can be derived from short, cheap, widely available and non-invasive tremor recordings, and is independent of operator or postural context in its interpretation.