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Author: Karsten Müller2
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, K. Mueller, 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

Publication Type: Journal article

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: K. Mueller, K. Arelin, H. E. Moller, J. Sacher, J. Kratzsch, T. Luck, S. Riedel-Heller, A. Villringer, M. L. Schroeter

Date Published: 2nd Feb 2016

Publication Type: Not specified

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