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David Mehler
David Mehler
RWTH Aachen & University of Münster. E-mail: mehlerdma@gmail
Verified email at uni-muenster.de
Title
Cited by
Cited by
Year
Open Science challenges, benefits and tips in early career and beyond
CPG Allen, DMA Mehler
PloS Biology, 2019
6522019
Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)
T Ros, S Enriquez-Geppert, V Zotev, KD Young, G Wood, ...
Brain 143 (6), 1674-1685, 2020
3132020
ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing
L Schmaal, E Pozzi, T C. Ho, LS Van Velzen, IM Veer, N Opel, ...
Translational psychiatry 10 (1), 172, 2020
1722020
Targeting the affective brain—a randomized controlled trial of real-time fMRI neurofeedback in patients with depression
DMA Mehler, MO Sokunbi, I Habes, K Barawi, L Subramanian, M Range, ...
Neuropsychopharmacology 43 (13), 2578-2585, 2018
1452018
Neurofeedback: One of today's techniques in psychiatry?
M Arns, JM Batail, S Bioulac, M Congedo, C Daudet, D Drapier, T Fovet, ...
Encephale 2 (43), 135-145, 2017
1292017
Evidence for adaptive cortical changes in swallowing in Parkinson’s disease
S Suntrup, I Teismann, J Bejer, I Suttrup, M Winkels, D Mehler, C Pantev, ...
Brain 136 (3), 726-738, 2013
1212013
The potential of functional near-infrared spectroscopy-based neurofeedback—a systematic review and recommendations for best practice
SH Kohl, DMA Mehler, M Lührs, RT Thibault, K Konrad, B Sorger
Frontiers in neuroscience 14, 594, 2020
1182020
Systematic misestimation of machine learning performance in neuroimaging studies of depression
C Flint, M Cearns, N Opel, R Redlich, DMA Mehler, D Emden, NR Winter, ...
Neuropsychopharmacology 46 (8), 1510-1517, 2021
1032021
Appreciating the significance of non-significant findings in psychology
DMA Mehler, PA Edelsbrunner, K Matić
Journal of European Psychology Students 10 (4), 1-7, 2019
782019
Neurofeedback training in major depressive disorder: a systematic review of clinical efficacy, study quality and reporting practices
LR Trambaiolli, SH Kohl, DEJ Linden, DMA Mehler
Neuroscience & Biobehavioral Reviews 125, 33-56, 2021
722021
The lure of misleading causal statements in functional connectivity research
DMA Mehler, KP Kording
arXiv preprint arXiv:1812.03363, 2018
64*2018
Beyond reporting statistical significance: Identifying informative effect sizes to improve scientific communication
PHP Hanel, DMA Mehler
Public understanding of science 28 (4), 468-485, 2019
622019
May the power be with you: are there highly powered studies in neuroscience, and how can we get more of them?
J Algermissen, DMA Mehler
Journal of Neurophysiology 119 (6), 2114-2117, 2018
602018
The BOLD response in primary motor cortex and supplementary motor area during kinesthetic motor imagery based graded fMRI neurofeedback
DMA Mehler, AN Williams, F Krause, M Lührs, RG Wise, DL Turner, ...
Neuroimage 184, 36-44, 2019
532019
Neurofeedback and the aging brain: a systematic review of training protocols for dementia and mild cognitive impairment
L Trambaiolli, R Cassani, DMA Mehler, T Falk
Frontiers in Aging Neuroscience, 2021
372021
Temporal stability and state-dependence of retrospective self-reports of childhood maltreatment in healthy and depressed adults.
J Goltermann, S Meinert, C Hülsmann, K Dohm, D Grotegerd, R Redlich, ...
Psychological assessment 35 (1), 12, 2023
31*2023
Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study
M Kirschner, B Hodzic-Santor, M Antoniades, I Nenadic, T Kircher, A Krug, ...
Molecular psychiatry 27 (2), 1167-1176, 2022
312022
Effective self-management for early career researchers in the natural and life sciences
NZ Bielczyk, A Ando, AP Badhwar, C Caldinelli, M Gao, A Haugg, ...
Neuron 106 (2), 212-217, 2020
292020
Graded fMRI neurofeedback training of motor imagery in middle cerebral artery stroke patients: a preregistered proof-of-concept study
DMA Mehler, AN Williams, JR Whittaker, F Krause, M Lührs, S Kunas, ...
Frontiers in human neuroscience 14, 226, 2020
27*2020
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
V Belov, T Erwin-Grabner, M Aghajani, A Aleman, AR Amod, Z Basgoze, ...
Scientific reports 14 (1), 1084, 2024
18*2024
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