Predicting response to (cognitive) interventions
There appears to be great variability in the response to treatment among participants and the reason for this variability remains unclear. Given that interventions are resource intensive, the characterization and selection of those participants who might beneﬁt from treatment would greatly improve the practicability and effectiveness of those interventions. In this project, we seek to find variables that enable a prediction of treatment response. Possible prognostic variables might be the volume of certain brain areas, markers of plasticity, or physiological variables.
Peter J, Schumacher LV, Landerer V, Abdulkadir A, Kaller CP, Lahr J, Klöppel S.
Biological factors contributing to the response to cognitive training in mild cognitive impairment.
J Alzheimers Dis. 2018;61(1):333-345. doi: 10.3233/JAD-170580.
Tablet-based cognitive intervention
Healthy aging is often accompanied by decline in cognitive functioning. Pathological brain changes that occur in neurodegenerative disorders (e.g., dementia) massively steepen this cognitive decline and lead to problems with activities of daily living.
In an ongoing study we will investigate whether a tablet-based cognitive training can improve cognitive performance in patients with dementia as well as in participants at increased risk of dementia. We will test the efficacy of an in-house developed tablet-based cognitive training that can be used at home. There will also be weekly on site sessions in small groups.
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In this research project, we aim to investigate if non-invasive brain stimulation techniques are able to modulate (or improve) cognitive interventions. We stimulate certain brain areas of individuals while they receive a cognitive intervention (e.g., a computerized cognitive training).
Vemuri P, Fields J, Peter J, Klöppel S.
Cognitive interventions in Alzheimer's and Parkinson's diseases: emerging mechanisms and role of imaging.
Curr Opin Neurol. 2016 Aug;29(4):405-11. doi: 10.1097/WCO.0000000000000346.
Testing the underlying mechanisms of learning
What are the underlying mechanisms of learning? This question will be addressed in this research project by simultaneuosly applying non-invasive brain stimulation techniques and brain imaging. With this combination, the underlying mechanisms can be depicted during learning.
Habich A, Canals S, Klöppel S.
Tuning noninvasive brain stimulation with MRI to cope with intersubject variability.
Curr Opin Neurol. 2016 Aug;29(4):453-8. doi: 10.1097/WCO.0000000000000353.
Peter J, Lahr J, Minkova L, ..., Klöppel S.
Contribution of the Cholinergic System to Verbal Memory Performance in Mild Cognitive Impairment.
J Alzheimers Dis. 2016 Jun 18;53(3):991-1001. doi: 10.3233/JAD-160273.
Lahr J, Peter J, Minkova L, ..., Klöppel S.
No difference in paired associative stimulation induced cortical neuroplasticity between patients with mild cognitive impairment and elderly controls.
Clin Neurophysiol. 2016 Feb;127(2):1254-60. doi: 10.1016/j.clinph.2015.08.010.
Klöppel S, Lauer E, Peter J, ..., Lahr J.
LTP-like plasticity in the visual system and in the motor system appear related in young and healthy subjects.
Front Hum Neurosci. 2015 Sep 24;9:506. doi: 10.3389/fnhum.2015.00506.
Machine learning for MRI-based dementia diagnosis and image segmentation
We develop and apply automated tools for the analysis of magnetic resonance imaging data. The methods include multivariate pattern recognition techniques to assist individual diagnosis, regression model to account for inter-subject variability, and deep learning to segment various structures in the brain. We regularly assess the potential of our methods to translate to clinical applications.
 Abdulkadir, Ahmed; Ronneberger, Olaf; Tabrizi, Sarah J.; Kloppel, Stefan (2014): Reduction of confounding effects with voxel-wise Gaussian process regression in structural MRI. In : 2014 International Workshop on Pattern Recognition in Neuroimaging. 2014 International Workshop on Pattern Recognition in Neuroimaging (PRNI). Tubingen, Germany, 04/06/2014 - 06/06/2014: IEEE, pp. 1–4. DOI: 10.1109/PRNI.2014.6858505.
 Çiçek, Özgün; Abdulkadir, Ahmed; Lienkamp, Soeren S.; Brox, Thomas; Ronneberger, Olaf (2016): 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Available online at http://arxiv.org/pdf/1606.06650v1.
 Falk, Thorsten; Mai, Dominic; Bensch, Robert; Çiçek, Özgün; Abdulkadir, Ahmed; Marrakchi, Yassine et al. (2018): U-Net: deep learning for cell counting, detection, and morphometry. In Nature Methods, p. 1. DOI: 10.1038/s41592-018-0261-2.
 Klöppel, Stefan; Kotschi, Maria; Peter, Jessica; Egger, Karl; Hausner, Lucrezia; Frölich, Lutz et al. (2018a): Separating symptomatic Alzheimer's Disease from depression based on structural MRI. In Journal of Alzheimer's disease : JAD (in press). https://content.iospress.com/articles/journal-of-alzheimers-disease/jad170964.
 Klöppel, Stefan; Yang, Shan; Kellner, Elias; Reisert, Marco; Heimbach, Bernhard; Urbach, Horst et al. (2018b): Voxel-wise deviations from healthy aging for the detection of region-specific atrophy. In NeuroImage: Clinical 20, pp. 851–860. DOI: 10.1016/j.nicl.2018.09.013.
 Kostro, Daniel; Abdulkadir, Ahmed; Durr, Alexandra; Roos, Raymund; Leavitt, Blair R.; Johnson, Hans et al. (2014): Correction of inter-scanner and within-subject variance in structural MRI based automated diagnosing. In NEUROIMAGE 98, pp. 405–415. DOI: 10.1016/j.neuroimage.2014.04.057.
 Schmitter, Daniel; Roche, Alexis; Maréchal, Bénédicte; Ribes, Delphine; Abdulkadir, Ahmed; Bach-Cuadra, Meritxell et al. (2015): An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease. In NeuroImage. Clinical 7, pp. 7–17. DOI: 10.1016/j.nicl.2014.11.001.
Improving slow wave sleep to enhance memory consolidation
In collaboration with research group Nissen:
Better quality of slow wave sleep (SWS) is connected to better post-sleep memory performance both in young and older adults. Disrupted SWS in cognitively healthy older adults has recently been suggested as an early biomarker of imminent cognitive decline. In a current project, we utilize closed-loop acoustic stimulation across multiple nights to enhance SWS in healthy older adults and in those at risk for dementia. We expect memory function to increase with the duration of the intervention. Individuals with relatively diminished SWS at baseline and/or individuals with increased risk for dementia should benefit the most. The long-term goal of this project is to test the suitability of closed loop acoustic stimulation to sustainably stabilize SWS in older adults to slow the progression towards dementia. Acoustic stimulation during SWS could therefore prove itself as a non-invasive and inexpensive tool to combat cognitive decline.