1. Home
  2. People Index
  3. Karsten Müller

Karsten Müller

About Karsten Müller:
No description specified

LHA-ID: 7WDP7C3Y2W-9

Location:  Germany

Expertise: Not specified

Tools: Not specified

ORCID: Not specified

help Karsten's tags

This person has not yet tagged anything.

Related items

LIFE- Leipzig Research Center for Civilization Diseases: inactive since

Goal of the Leipzig Research Center for Civilization Diseases (LIFE) is the investigation of civilization diseases like depression, diabetes, allergies or cardiovascular diseases. For this purpose we collect as much data as possible regarding health and living conditions of the population in Leipzig and provide these data for scientists of the University of Leipzig and other research institutions.

Programme: Epidemiology

Public web page: http://life.uni-leipzig.de/

Start date: 1st Jan 2009

LIFE Adult

This population-based study examined 10,000 participants randomly selected from the Leipzig population (2011 to 2014). A follow-up is to be carried out from 2017 - 2020. The study mainly included people aged between 40 and 79. All participants underwent a 6-hour study program and people over 60 years of age were invited two more times to in-depth study of cognition and depression and the brain (MRI, EEG). Extensive measurements of genome, metabolome and transcriptome are available. The LIFE-ADULT
...

Programme: Epidemiology

Public web page: http://life.uni-leipzig.de/en/adults/life_adult.html

Start date: 1st Jan 2009

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data.
LIFE Adult

Abstract (Expand)

PURPOSE: Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep … alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. MATERIALS & METHODS: Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, "leave one center out" conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis. RESULTS: Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach. CONCLUSION: Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

Authors: S. Meyer, Karsten Müller, K. Stuke, S. Bisenius, J. Diehl-Schmid, F. Jessen, J. Kassubek, J. Kornhuber, A. C. Ludolph, J. Prudlo, A. Schneider, K. Schuemberg, I. Yakushev, M. Otto, M. L. Schroeter

Date Published: 29th Mar 2017

Journal: Neuroimage Clin

Human Diseases: frontotemporal dementia

Serum BDNF correlates with connectivity in the (pre)motor hub in the aging human brain--a resting-state fMRI pilot study.
LIFE Adult

Abstract (Expand)

Brain-derived neurotrophic factor (BDNF) has been discussed to be involved in plasticity processes in the human brain, in particular during aging. Recently, aging and its (neurodegenerative) diseases … have increasingly been conceptualized as disconnection syndromes. Here, connectivity changes in neural networks (the connectome) are suggested to be the most relevant and characteristic features for such processes or diseases. To further elucidate the impact of aging on neural networks, we investigated the interaction between plasticity processes, brain connectivity, and healthy aging by measuring levels of serum BDNF and resting-state fMRI data in 25 young (mean age 24.8 +/- 2.7 (SD) years) and 23 old healthy participants (mean age, 68.6 +/- 4.1 years). To identify neural hubs most essentially related to serum BDNF, we applied graph theory approaches, namely the new data-driven and parameter-free approach eigenvector centrality (EC) mapping. The analysis revealed a positive correlation between serum BDNF and EC in the premotor and motor cortex in older participants in contrast to young volunteers, where we did not detect any association. This positive relationship between serum BDNF and EC appears to be specific for older adults. Our results might indicate that the amount of physical activity and learning capacities, leading to higher BDNF levels, increases brain connectivity in (pre)motor areas in healthy aging in agreement with rodent animal studies. Pilot results have to be replicated in a larger sample including behavioral data to disentangle the cause for the relationship between BDNF levels and connectivity.

Authors: Karsten Müller, K. Arelin, H. E. Moller, J. Sacher, J. Kratzsch, Tobias Luck, Steffi Gerlinde Riedel-Heller, A. Villringer, Markus Kreuz

Date Published: 2nd Feb 2016

Journal: Neurobiol Aging

Human Diseases: Not specified

Navigate

Leipzig Health Atlas

The Leipzig Health Atlas is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

Registered Repository

re3data.org Badge
Powered by
 (v.1.9.1)
Leipzig Health Atlas | Funding and Programmes | Credits | Terms & Conditions | Privacy Policy | Imprint
Copyright © 2008 - 2019 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig