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Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M  = 73.8 years; SD  = 9.0). Of these, 68 patients ( M  = 73.9 years; SD  = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p  < .001; β = .274; P  = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P  = .035) those with greater functional coupling in interhemispheric connections declined more (β = -.402; P  = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior-posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.

Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics. To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models. For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model. In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

Background: Patient dependence has rarely been studied in mild-to-moderate Alzheimer's disease (AD)., Objective: To identify factors which predict patient dependence in mild-to-moderate AD., Methods: We studied 398 non-institutionalized AD patients (234 females) of the ongoing Prospective Registry on Dementia (PRODEM) in Austria. The Dependence Scale (DS) was used to assess patient dependence. Patient assessment comprised functional abilities, neuropsychiatric symptoms and cognitive functions. A multiple linear regression analysis was performed to identify predictors of patient dependence., Results: AD patients were mildly-to-moderately impaired (mean scores and SDs were: CDR 0.84 ± 0.43; DAD 74.4 ± 23.3, MMSE = 22.5 ± 3.6). Psychopathology and caregiver burden were in the low range (mean NPI score 13.2, range 0 to 98; mean ZBI score 18, range 0-64). Seventy five percent of patients were classified as having a mild level of patient dependence (DS sum score 0 to 6). Patient dependence correlated significantly and positively with age, functional measures, psychopathology and depression, disease duration, and caregiver burden. Significant negative, but low correlations were found between patient dependence, cognitive variables, and global cognition. Activities of daily living, patient age, and disease severity accounted for 63% of variance in patient dependence, whereas cognitive variables accounted for only 11%., Conclusion: Dependence in this cohort was mainly related to age and functional impairment, and less so to cognitive and neuropsychiatric variables. This differs from studies investigating patients in more advanced disease stages which found abnormal behavior and impairments of cognition as main predictors of patient dependence.

Objective: To assess the influence of cognitive, functional and behavioral factors, co-morbidities as well as caregiver characteristics on driving cessation in dementia patients., Methods: The study cohort consists of those 240 dementia cases of the ongoing prospective registry on dementia in Austria (PRODEM) who were former or current car-drivers (mean age 74.2 (±8.8) years, 39.6% females, 80.8% Alzheimer's disease). Reasons for driving cessation were assessed with the patients' caregivers. Standardized questionnaires were used to evaluate patient- and caregiver characteristics. Cognitive functioning was determined by Mini-Mental State Examination (MMSE), the CERAD neuropsychological test battery and Clinical Dementia Rating (CDR), activities of daily living (ADL) by the Disability Assessment for Dementia, behavior by the Neuropsychiatric Inventory (NPI) and caregiver burden by the Zarit burden scale., Results: Among subjects who had ceased driving, 136 (93.8%) did so because of "Unacceptable risk" according to caregiver's judgment. Car accidents and revocation of the driving license were responsible in 8 (5.5%) and 1(0.7%) participant, respectively. Female gender (OR 5.057; 95%CI 1.803-14.180; p = 0.002), constructional abilities (OR 0.611; 95%CI 0.445-0.839; p = 0.002) and impairment in Activities of Daily Living (OR 0.941; 95%CI 0.911-0.973; p<0.001) were the only significant and independent associates of driving cessation. In multivariate analysis none of the currently proposed screening tools for assessment of fitness to drive in elderly subjects including the MMSE and CDR were significantly associated with driving cessation., Conclusion: The risk-estimate of caregivers, but not car accidents or revocation of the driving license determines if dementia patients cease driving. Female gender and increasing impairment in constructional abilities and ADL raise the probability for driving cessation. If any of these factors also relates to undesired traffic situations needs to be determined before recommendations for their inclusion into practice parameters for the assessment of driving abilities in the elderly can be derived from our data.

We conducted an open, 16-week study on the efficacy of memantine on behavioral disturbances and psychotic symptoms in moderate to moderately severe Alzheimer s disease in daily routine. Fifty-three patients of 20 outpatient centers in Austria were recruited. The Neuropsychiatric Inventory (NPI) was defined as main outcome measure. After 16 weeks the total NPI score improved by 4,6 points (p<0.01). The caregiver distress score was also significantly reduced. The most pronounced improvements were seen in the NPI components depression (-24,6%), aberrant motor behavior (-16,9%), agitation/agression, fear, apathy, disinhibition and disturbances in appetite and eating behavior (-11,3%, each). Our naturalistic study is in line with the results of controlled trials in moderate and severe Alzheimer dementia stages. Controlled clinical trials which have behavioral disturbances and psychotic symptoms as primary endpoint are needed to define the true potential of memantine in mild dementia stages.

We performed a 6-month open-label study on the use of the transdermal rivastigmine patch in clinical routine in 103 patients with Alzheimer's disease from 25 outpatient services in Austria. After baseline, safety and tolerability of the 10 cm2--rivastigmine patch was assessed at week 4, 12 and 24 in all patients. A Mini Mental State Examination was done at baseline and at week 12 and 24. Skin adherence of the patch was very good or good in 85% of study participants. Only 2.9% of patients had gastrointestinal adverse events. Local skin reactions occurred in 23% of individuals. Skin alteration were mostly mild in severity. In only 6.8% of subjects did they result in termination of treatment. At the earliest skin reactions were observed after 3 months of treatment. Cognitive functioning of patients improved comparable to the controlled trial which led to approval of the rivastigmine patch. In daily routine the safety profile of the rivastigmine patch is favourable, as is the response to treatment. Local, mostly mild skin reactions affect approximately every fifth patient, and they occur relatively late in the course of therapy. Patients and their caregivers should receive detailed information about skin reactions to omit unnecessary drop outs to treatment.