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Ingo Kilimann

Other affiliations: University of Tübingen
Bio: Ingo Kilimann is an academic researcher from German Center for Neurodegenerative Diseases. The author has contributed to research in topics: Dementia & Medicine. The author has an hindex of 17, co-authored 35 publications receiving 3363 citations. Previous affiliations of Ingo Kilimann include University of Tübingen.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).
Abstract: 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).

3,691 citations

Journal ArticleDOI
TL;DR: The data of this study suggest that BFCS morphometry may provide an emerging biomarker in AD, and between-center reliability and diagnostic accuracy of MRI-based B FCS volumetry in a large multicenter data set.
Abstract: Histopathological studies in Alzheimer's 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.

147 citations

Journal ArticleDOI
TL;DR: Dementia care management provided by specifically trained nurses is an effective collaborative care model that improves relevant patient- and caregiver-related outcomes in dementia and should become an active area of research.
Abstract: Importance Dementia care management (DCM) can increase the quality of care for people with dementia. Methodologically rigorous clinical trials on DCM are lacking. Objective To test the effectiveness and safety of DCM in the treatment and care of people with dementia living at home and caregiver burden (when available). Design, Setting, and Participants This pragmatic, general practitioner–based, cluster-randomized intervention trial compared the intervention with care as usual at baseline and at 12-month follow-up. Simple 1:1 randomization of general practices in Germany was used. Analyses were intent to treat and per protocol. In total, 6838 patients were screened for dementia (eligibility: 70 years and older and living at home) from January 1, 2012, to March 31, 2016. Overall, 1167 (17.1%) were diagnosed as having dementia, and 634 (9.3%) provided written informed consent to participate. Interventions Dementia care management was provided for 6 months at the homes of patients with dementia. Dementia care management is a model of collaborative care, defined as a complex intervention aiming to provide optimal treatment and care for patients with dementia and support caregivers using a computer-assisted assessment determining a personalized array of intervention modules and subsequent success monitoring. Dementia care management was targeted at the individual patient level and was conducted by 6 study nurses with dementia care–specific qualifications. Main Outcomes and Measures Quality of life, caregiver burden, behavioral and psychological symptoms of dementia, pharmacotherapy with antidementia drugs, and use of potentially inappropriate medication. Results The mean age of 634 patients was 80 years. A total of 407 patients received the intended treatment and were available for primary outcome measurement. Of these patients, 248 (60.9%) were women, and 204 (50.1%) lived alone. Dementia care management significantly decreased behavioral and psychological symptoms of dementia (b = −7.45; 95% CI, −11.08 to −3.81; P P = .045) compared with care as usual. Patients with dementia receiving DCM had an increased chance of receiving antidementia drug treatment (DCM, 114 of 291 [39.2%] vs care as usual, 31 of 116 [26.7%]) after 12 months (odds ratio, 1.97; 95% CI, 0.99 to 3.94; P = .03). Dementia care management significantly increased quality of life (b = 0.08; 95% CI, 0 to 0.17; P = .03) for patients not living alone but did not increase quality of life overall. There was no effect on potentially inappropriate medication (odds ratio, 1.86; 95% CI, 0.62 to 3.62; P = .97). Conclusions and Relevance Dementia care management provided by specifically trained nurses is an effective collaborative care model that improves relevant patient- and caregiver-related outcomes in dementia. Implementing DCM in different health care systems should become an active area of research. Trial Registration clinicaltrials.gov Identifier:NCT01401582

100 citations

Journal ArticleDOI
31 May 2013-PLOS ONE
TL;DR: 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 is supported.
Abstract: 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 Naive 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.

99 citations

Journal ArticleDOI
TL;DR: This study aimed to assess how interindividual differences in locus coeruleus (LC) magnetic resonance imaging (MRI) contrast relate to cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD).

62 citations


Cited by
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Journal ArticleDOI
TL;DR: The Lancet Commission on Dementia Prevention, Intervention, and Care met to consolidate the huge strides that have been made and the emerging knowledge as to what the authors should do to prevent and manage dementia.

3,826 citations

Journal ArticleDOI
TL;DR: Author(s): Livingston, Gill; Huntley, Jonathan; Sommerlad, Andrew ; Sommer Glad, Andrew; Ames, David; Ballard, Clive; Banerjee, Sube; Brayne, Carol; Burns, Alistair; Cohen-Mansfield, Jiska; Cooper, Claudia; Costafreda, Sergi G; Dias, Amit; Fox, Nick; Gitlin, Laura N; Howard, Robert; Kales, Helen C;

3,559 citations

21 Jun 2010

1,966 citations