The criteria ought to provide the foundation for research work into the neuropsychology, neuropathology, genetics, molecular biology, and epidemiology of these important clinical disorders that account for a substantial proportion of cases of primary degenerative dementia occurring before the age of 65 years.

BackgroundTo evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early-stage Alzheimer's disease (AD).MethodsHealthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their “power” to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy.ResultsThe classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% ± 5%; between HCs and those with AD, 87% ± 3%; and between those with MCI and those with AD, 80% ± 5%, demonstrating its assessment utility.ConclusionAutomatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline.

These findings challenge current management practices of Alzheimer's patients. While cognitive-oriented group therapies have gained popularity, this trial does not show improvement for the patients. The individualized cognitive rehabilitation intervention provided clinically significant results. Individual interventions should be considered to delay institutionalization in Alzheimer's disease.

Plasma Aβ was associated with cognitive status and CSF biomarkers, suggesting the interest of plasma amyloid biomarkers for diagnosis purpose.

Background: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. Methods: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer’s or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. Results: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). Conclusion: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.