Characteristics of subjective cognitive decline associated with amyloid positivity

Introduction: The evidence for characteristics of persons with subjective cognitive decline (SCD) associated with amyloid positivity is limited. Methods: In 1640 persons with SCD from 20 Amyloid Biomarker Study cohort, we investigated the associations of SCD-specific characteristics (informant confirmation, domain-specific complaints, concerns, feelings of worse performance) demographics, setting, apolipoprotein E gene (APOE) ε4 carriership, and neuropsychiatric symptoms with amyloid positivity. Results: Between cohorts, amyloid positivity in 70-year-olds varied from 10% to 76%. Only older age, clinical setting, and APOE ε4 carriership showed univariate associations with increased amyloid positivity. After adjusting for these, lower education was also associated with increased amyloid positivity. Only within a research setting, informant-confirmed complaints, memory complaints, attention/concentration complaints, and no depressive symptoms were associated with increased amyloid positivity. Feelings of worse performance were associated with less amyloid positivity at younger ages and more at older ages. Discussion: Next to age, setting, and APOE ε4 carriership, SCD-specific characteristics may facilitate the identification of amyloid-positive individuals

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Supplementary Material for: Prognostic Accuracy of Mild Cognitive Impairment Subtypes at Different Cut-Off Levels

<p><b><i>Background/Aims:</i></b> The prognostic accuracy of mild cognitive impairment (MCI) in clinical settings is debated, variable across criteria, cut-offs, subtypes, and follow-up time. We aimed to estimate the prognostic accuracy of MCI and the MCI subtypes for dementia using three different cut-off levels. <b><i>Methods:</i></b> Memory clinic patients were followed for 2 (<i>n</i> = 317, age 63.7 ± 7.8) and 4-6 (<i>n</i> = 168, age 62.6 ± 7.4) years. We used 2.0, 1.5, and 1.0 standard deviations (SD) below the mean of normal controls (<i>n</i> = 120, age 64.1 ± 6.6) to categorize MCI and the MCI subtypes. Prognostic accuracy for dementia syndrome at follow-up was estimated. <b><i>Results:</i></b> Amnestic multi-domain MCI (aMCI-md) significantly predicted dementia under all conditions, most markedly when speed/attention, language, or executive function was impaired alongside memory. For aMCI-md, sensitivity increased and specificity decreased when the cut-off was lowered from 2.0 to 1.5 and 1.0 SD. Non-subtyped MCI had a high sensitivity and a low specificity. <b><i>Conclusion:</i></b> Our results suggest that aMCI-md is the only viable subtype for predicting dementia for both follow-up times. Lowering the cut-off decreases the positive predictive value and increases the negative predictive value of aMCI-md. The results are important for understanding the clinical prognostic utility of MCI, and MCI as a non-progressive disorder.</p