There is a need for increased nosological knowledge to enable rational trials in Alzheimer's disease (AD) and related disorders. The ongoing Gothenburg mild cognitive impairment (MCI) study is an attempt to conduct longitudinal indepth phenotyping of patients with different forms and degrees of cognitive impairment using neuropsychological, neuroimaging, and neurochemical tools. Particular attention is paid to the interplay between AD and subcortical vascular disease, the latter representing a disease entity that may cause or contribute to cognitive impairment with an effect size that may be comparable to AD. Of 664 patients enrolled between 1999 and 2013, 195 were diagnosed with subjective cognitive impairment (SCI), 274 with mild cognitive impairment (MCI), and 195 with dementia, at baseline. Of the 195 (29%) patients with dementia at baseline, 81 (42%) had AD, 27 (14%) SVD, 41 (21%) mixed type dementia (¼AD þ SVD ¼ MixD), and 46 (23%) other etiologies. After 6 years, 292 SCI/MCI patients were eligible for followup. Of these 292, 69 (24%) had converted to dementia (29 (42%) AD, 16 (23%) SVD, 15 (22%) MixD, 9 (13%) other etiologies). The study has shown that it is possible to identify not only AD but also incipient and manifest MixD/SVD in a memory clinic setting. These conditions should be taken into account in clinical trials.

Recent work has indicated the potential utility of automated language analysis for the detection of mild cognitive impairment (MCI). Most studies combining language processing and machine learning for the prediction of MCI focus on a single language task; here, we consider a cascaded approach to combine data from multiple language tasks. A cohort of 26 MCI participants and 29 healthy controls completed three language tasks: picture description, reading silently, and reading aloud. Information from each task is captured through different modes (audio, text, eye-tracking, and comprehension questions). Features are extracted from each mode, and used to train a series of cascaded classifiers which output predictions at the level of features, modes, tasks, and finally at the overall session level. The best classification result is achieved through combining the data at the task level (AUC = 0.88, accuracy = 0.83). This outperforms a classifier trained on neuropsychological test scores (AUC = 0.75, accuracy = 0.65) as well as the “early fusion” approach to multimodal classification (AUC = 0.79, accuracy = 0.70). By combining the predictions from the multimodal language classifier and the neuropsychological classifier, this result can be further improved to AUC = 0.90 and accuracy = 0.84. In a correlation analysis, language classifier predictions are found to be moderately correlated (ρ = 0.42) with participant scores on the Rey Auditory Verbal Learning Test (RAVLT). The cascaded approach for multimodal classification improves both system performance and interpretability. This modular architecture can be easily generalized to incorporate different types of classifiers as well as other heterogeneous sources of data (imaging, metabolic, etc.).

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Background/Aims: In the quest for prevention or treatment, there is a need to find early markers for preclinical dementia. This study observed memory clinic patients with subjective cognitive impairment (SCI) and normal cognitive function at baseline. The primary aim was to address SCI as a potential risk factor for cognitive decline. The secondary aim was to address a potential relation between (1) baseline cerebrospinal fluid biomarkers and (2) a decline in memory performance over the first 2 years of follow-up, with a possible cognitive decline after 6 years. Methods: Eighty-one patients (mean age 61 years) were recruited from university memory clinics and followed up for 6 years. Results: Eighty-six percent of the cohort remained cognitively stable or improved, 9% developed mild cognitive impairment, and only 5% (n = 4) developed dementia. Regression analysis revealed that low levels of Aβ₄₂ at baseline and memory decline during the first 2 years predicted dementia. When combined, these variables were associated with a 50% risk of developing dementia. Conclusions: Cognitive stability for 86% of the cohort suggests that SCI is predominantly a benign condition with regard to neuropathology. The low number of individuals who developed dementia limits the generalizability of the results and discussion of progression factors.

Mild Cognitive Impairment (MCI) is a syndrome characterized by cognitive decline greater than expected for an individual's age and education level. This study aims to determine whether voice quality and speech fluency distinguish patients with MCI from healthy individuals to improve diagnosis of patients with MCI. We analyzed recordings of the Cookie Theft picture description task produced by 26 patients with MCI and 29 healthy controls from Sweden and calculated measures of voice quality and speech fluency. The results show that patients with MCI differ significantly from HC with respect to acoustic aspects of voice quality, namely H1-A3, cepstral peak prominence, center of gravity, and shimmer; and speech fluency, namely articulation rate and averaged speaking time. The method proposed along with the obtainability of connected speech productions can enable quick and easy analysis of speech fluency and voice quality, providing accessible and objective diagnostic markers of patients with MCI.