Ingo Kilimann

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

Summary Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).

Subregional Basal Forebrain Atrophy in Alzheimer's Disease: A Multicenter Study

Histopathological studies in Alzheimers disease (AD) suggest severe and region-specific neurodegeneration of the basal forebrain cholinergic system (BFCS). Here, we studied the between-center reliability and diagnostic accuracy of MRI-based BFCS volumetry in a large multicenter data set, including participants with prodromal (n = 41) or clinically manifest AD (n = 134) and 148 cognitively healthy controls. Atrophy was determined using voxel-based and region-of-interest based analyses of high-dimensionally normalized MRI scans using a newly created map of the BFCS based on postmortem in cranio MRI and histology. The AD group showed significant volume reductions of all subregions of the BFCS, which were most pronounced in the posterior nucleus basalis Meynert (NbM). The mild cognitive impairment-AD group showed pronounced volume reductions in the posterior NbM, but preserved volumes of anterior-medial regions. Diagnostic accuracy of posterior NbM volume was superior to hippocampus volume in both groups, despite higher multicenter variability of the BFCS measurements. The data of our study suggest that BFCS morphometry may provide an emerging biomarker in AD.

Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data.

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.

Medication management for people with dementia in primary care: description of implementation in the DelpHi study

BackgroundAs the population ages, the relative and absolute number of age-associated diseases such as dementia will increase. Evaluation of the suitability and intake of medication and pharmacological treatment is an important aspect of care for people with dementia, especially if they live at home. Regular medication reviews and systematic cooperation between physicians and pharmacists are not common in routine care. Medication management (MM), based on such a comprehensive home medication review could help to reduce drug-related problems and costs. The present article presents a medication management specifically for the application in the ambulatory setting and describes its implementation as part of a larger trial.Methods/designA home medication review (HMR) and MM is implemented as part of the DelpHi study, a population based prospective, cluster-randomized controlled intervention study to test the efficacy and efficiency of the implementation of a collaborative care model in primary care.Participants: people with dementia (PWD) and their caregivers are recruited by the patient’s general practitioner. Inclusion criteria are a positive screening result for dementia, living at home and regular intake of drugs. PWD are asked to specify their regular pharmacy which is asked to participate in the study, too.Intervention: a comprehensive HMR is conducted as computer-assisted personal interview by specifically qualified Dementia Care Manager (DCM) at the people’s home. It includes detailed information about drugs taken, their storage, administration, adherence and adverse events. The MM is conducted in cooperation between DCM, pharmacist and general practitioner and consists of a pharmaceutical evaluation, pharmaceutical recommendations and their application. Pharmacists are trained and provided with regularly updated information. The MM is designed to give information and recommendations concerning antidementia drugs, occurrence of drug related problems, intake of anticholinergic drugs, potentially clinically relevant drug-drug-interactions, adverse drug events and medication adherence.DiscussionThe DelpHi-approach for medication management employs comprehensive instruments and procedures in the primary care setting under routine care conditions, and this approach should be useful in improving pharmacotherapy as part of the comprehensive treatment and care for people with dementia.Trial registrationThe trial is registered at ClinicalTrials.gov, number NCT01401582.

Potentially Inappropriate Medication in Community-Dwelling Primary Care Patients who were Screened Positive for Dementia

Background: Potentially inappropriate medication (PIM) in older people is a risk factor for adverse drug effects. This risk is even higher in older people with dementia (PWD). Objective: Our study aimed to determine (1) the prevalence of PIM among primary care patients who were screened positive for dementia and (2) the sociodemographic and clinical variables associated with the use of PIM. Methods: DelpHi-MV (Dementia: life- and person-centered help in Mecklenburg–Western Pomerania) is a general practitioner-based, cluster-randomized, controlled intervention study to implement and evaluate an innovative concept of collaborative dementia care management in Germany. The comprehensive baseline assessment includes a home medication review. The present analyses are based on the data from 448 study participants (age 70+, DemTect <9). PIMs were identified using the list of Potentially Inappropriate Medications in the Elderly (Priscus). Results: (1) A total of 99 study participants (22%) received at least one PIM. The highest prevalence was found for antidepressants, benzodiazepines, and analgetics. The most frequently prescribed PIMs were amitriptyline, etoricoxib, and doxazosin. (2) Use of a PIM was significantly associated with a diagnosis of a mental or behavioral disorder. Conclusions: The prescription rate of PIMs for community-dwelling PWD was comparable with the rates found for the general population of older people in Germany (20–29%). Antidepressants with anticholinergic properties and long-acting benzodiazepines were the most prescribed PIMs, despite having an unfavorable benefit-risk ratio. This high prevalence of PIM prescriptions in a vulnerable population of PWD indicates that standard care for dementia should include careful medication review and management.

Rates of formal diagnosis of dementia in primary care: The effect of screening

Screening could improve recognition of dementia in primary care. We sought to determine the effect of screening for dementia in primary care practices on the formal diagnosis rate; the distribution of differential diagnoses; and the factors associated with receiving a formal diagnosis.

Association of basal forebrain volumes and cognition in normal aging

The basal forebrain cholinergic system (BFCS) is known to undergo moderate neurodegenerative alterations during normal aging and severe atrophy in Alzheimers disease (AD). It has been suggested that functional and structural alterations of the BFCS mediate cognitive performance in normal aging and AD. But, it is still unclear to what extend age-associated cognitive decline can be related to BFCS in normal aging. We analyzed the relationship between BFCS volume and cognition using MRI and a comprehensive neuropsychological test battery in a cohort of 43 healthy elderly subjects spanning the age range from 60 to 85 years. Most notably, we found significant associations between general intelligence and BFCS volumes, specifically within areas corresponding to posterior nuclei of the nucleus basalis of Meynert (Ch4p) and the nucleus subputaminalis (NSP). Associations between specific cognitive domains and BFCS volumes were less pronounced. Supplementary analyses demonstrated that especially the volume of NSP but also the volume of Ch4p was related to the volume of widespread temporal, frontal, and parietal gray and white matter regions. Volumes of these gray and white matter regions were also related to general intelligence. Higher volumes of Ch4p and NSP may enhance the effectiveness of acetylcholine supply in related gray and white matter regions underlying general intelligence and hence explain the observed association between the volume of Ch4p as well as NSP and general intelligence. Since general intelligence is known to attenuate the degree of age-associated cognitive decline and the risk of developing late-onset AD, the BFCS might, besides the specific contribution to the pathophysiology in AD, constitute a mechanism of brain resilience in normal aging.

Combining DTI and MRI for the automated detection of alzheimer’s disease using a large european multicenter dataset

Diffusion tensor imaging (DTI) allows assessing neuronal fiber tract integrity in vivo to support the diagnosis of Alzheimers disease (AD). It is an open research question to which extent combinations of different neuroimaging techniques increase the detection of AD. In this study we examined different methods to combine DTI data and structural T1-weighted magnetic resonance imaging (MRI) data. Further, we applied machine learning techniques for automated detection of AD. We used a sample of 137 patients with clinically probable AD (MMSE 20.6 ±5.3) and 143 healthy elderly controls, scanned in nine different scanners, obtained from the recently created framework of the European DTI study on Dementia (EDSD). For diagnostic classification we used the DTI derived indices fractional anisotropy (FA) and mean diffusivity (MD) as well as grey matter density (GMD) and white matter density (WMD) maps from anatomical MRI. We performed voxel-based classification using a Support Vector Machine (SVM) classifier with tenfold cross validation. We compared the results from each single modality with those from different approaches to combine the modalities. For our sample, combining modalities did not increase the detection rates of AD. An accuracy of approximately 89% was reached for GMD data alone and for multimodal classification when GMD was included. This high accuracy remained stable across each of the approaches. As our sample consisted of mildly to moderately affected patients, cortical atrophy may be far progressed so that the decline in structural network connectivity derived from DTI may not add additional information relevant for the SVM classification. This may be different for predementia stages of AD. Further research will focus on multimodal detection of AD in predementia stages of AD, e.g. in amnestic mild cognitive impairment (aMCI), and on evaluating the classification performance when adding other modalities, e.g. functional MRI or FDG-PET.

Hearing Impairment Affects Dementia Incidence. An Analysis Based on Longitudinal Health Claims Data in Germany.

Recent research has revealed an association between hearing impairment and dementia. The objective of this study is to determine the effect of hearing impairment on dementia incidence in a longitudinal study, and whether ear, nose, and throat (ENT) specialist care, care level, institutionalization, or depression mediates or moderates this pathway. The present study used a longitudinal sample of 154,783 persons aged 65 and older from claims data of the largest German health insurer; containing 14,602 incident dementia diagnoses between 2006 and 2010. Dementia and hearing impairment diagnoses were defined according to International Classification of Diseases, Tenth Revision, codes. We used a Kaplan Meier estimator and performed Cox proportional hazard models to explore the effect of hearing impairment on dementia incidence, controlling for ENT specialist care, care level, institutionalization, and depression. Gender, age, and comorbidities were controlled for as potential confounders. Patients with bilateral (HR = 1.43, p<0.001) and side-unspecified (HR = 1.20, p<0.001) hearing impairment had higher risks of dementia incidence than patients without hearing impairment. We found no significant effect for unilateral hearing impairment and other diseases of the ear. The effect of hearing impairment was only partly mediated through ENT specialist utilization. Significant interaction between hearing impairment and specialist care, care level, and institutionalization, respectively, indicated moderating effects. We discuss possible explanations for these effects. This study underlines the importance of the association between hearing impairment and dementia. Preserving hearing ability may maintain social participation and may reduce the burden associated with dementia. The particular impact of hearing aid use should be the subject of further investigations, as it offers potential intervention on the pathway to dementia.

Regional Pattern of Dementia and Prevalence of Hearing Impairment in Germany

To determine the association between hearing impairment and dementia.