Detecting and Managing Neuropsychiatric Symptoms in Dementia

Measuring neuropsychiatric symptoms in patients with early cognitive decline using speech analysis

BACKGROUND

Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders.

METHODS

Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.

RESULTS

Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores.

CONCLUSIONS

Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.

Reliability and Validity of the Chinese Version of the Mild Behavioral Impairment Checklist for Screening for Alzheimer's Disease

BACKGROUND

The Mild Behavioral Impairment Checklist (MBI-C), a screening scale for neuropsychiatric symptom evaluation, facilitates Alzheimer's disease (AD) screening. However, its validity and reliability for use as an AD screening tool have not been determined.

OBJECTIVE

To develop an AD screening scale suitable for the Chinese population.

METHODS

The MBI-C was translated into Chinese and back-translated with the original author's consent. Forty-six AD patients, attending the Xuanwu hospital memory clinic, and 50 sex- and education-matched controls from the community underwent a full neuropsychological evaluation, including MBI-C assessment. Among them, 15 AD patients were evaluated repeatedly, and eight were evaluated simultaneously by two different clinicians, to assess MBI-C reliability.

RESULTS

The MBI-C demonstrated good internal consistency reliability, test-retest reliability, and inter-rater reliability. Its optimal cutoff point was 6/7 for identifying AD dementia, with a sensitivity of 86.96% and specificity of 86.00%, and its detection rate for moderate-severe AD dementia was higher than that of the Neuropsychiatric Inventory Questionnaire (NPI-Q). Pearson's correlation coefficients ranged from 0.702 to 0.831, indicating content validity. Seven factors were extracted during principal component analysis, with a cumulative contribution of 70.55%. Moreover, the Pearson's correlation coefficient was 0.758, indicating its criterion validity. The MBI-C could also distinguish AD dementia severity. MBI-C scores were significantly negatively correlated with MMSE and MoCA scores, and positively correlated with ADL scores.

CONCLUSION

This study showed that the Chinese version of MBI-C has high reliability and validity, and could replace the NPI-Q for AD dementia screening in the Chinese population.