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NEUROFEEDBACK

Real-time neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in psychiatric and neurological disorders which are associated with altered brain activity patterns. I used fMRI-based neurofeedback during emotion regulation and EEG-based neurofeedback during motor imagery.

FMRI NEUROFEEDBACK &
EMOTION REGULATION

fMRI-based neurofeedback has the advantage to probe cortical and subcortical structures. While working with Dr Kathrin Cohen Kadosh on BRAINTRAIN we investigated the feasibility of fMRI-based neurofeedback in adolescent girls. Specifically, we fed back the functional connectivity between the amygdala and the prefrontal cortex during an emotion regulation task. The study was registered as a preclinical trial. We showed that neurofeedback implementations differentially modulate functional connectivity (Zich et al., 2020). Further, we found that neurofeedback training affects emotion regulation and the neurotransmitter concentrations as measured with MRS moderate neurofeedback effects.  

Our data contributed to two meta-analyses (Haugg et al., 2020; Haugg et al., 2021). 

 

Related publications:

  • Sanders ZB, Fleming MK, Smejka T, Marzolla M, Zich C, Rieger S, Lührs M, Goebel R, Sampaio-Baptista C, Johansen-Berg H, 2022

Self-modulation of motor cortex activity after stroke: a randomized controlled trial

Brain. awac239. https://doi.org/10.1093/brain/awac239

  • Paret C, Goldway N, Zich C, Keynan JN, Hendler T, Linden D, Cohen Kadosh K, 2019

Current progress in real-time functional magnetic resonance-based neurofeedback: methodological challenges and achievements

NeuroImage 202 (116107), https://doi.org/10.1016/j.neuroimage.2019.116107​

  • ​​​Zich C, Johnstone N, Luehrs M, Lisk S, Haller SP, Lipp A, Lau JYF, Cohen Kadosh K, 2020

Modulatory effects of fMRI-based neurofeedback on neural and behavioural level during adolescence

NeuroImage. 220 (117053), https://doi.org/10.1016/j.neuroimage.2020.117053​

  • Lisk S, Cohen Kadosh K, Zich C, Haller SP, Lau JYF, 2020

Training negative connectivity patterns between the dorsolateral prefrontal cortex and amygdala through fMRI-based neurofeedback to target adolescent socially-avoidant behaviour

Behaviour Research and Therapy. 135, 103760, https://doi.org/10.1016/j.brat.2020.103760​

  • Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M, […], Zich C, […] Scharnowski F, 2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity? 

Hum Brain Mapp. 41(14): 3839-3854, https://doi.org/10.1002/hbm.25089​​

  • Haugg A, Renz FM, Nicholson AA, Lor C, Götzendorfer SJ, Sladky R, […], Zich C, […] Steyrl D, 2021 

Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis

NeuroImage, 118207, https://doi.org/10.1016/j.neuroimage.2021.118207​

EEG NEUROFEEDBACK &
MOTOR IMAGERY

Motor imagery in combination with neurofeedback has been suggested as a promising add-on rehabilitation approach. My work at the University of Oldenburg focussed on the following aspects:

 

Inter-individual and age-related differences  

Using multi-modal neuroimaging I validated EEG-based neurofeedback. I investigated inter-individual differences in EEG-based neurofeedback performance by acquiring simultaneous fMRI (Zich et al., 2015). We found that individuals that perform poorly in EEG-based neurofeedback, i.e., BCI illiterates, can be divided into two groups, real- and pseudo-BCI illiterates. This finding led to a new neurofeedback visualisation, feeding back the amount of hemispheric lateralisation and contralateral activity. Further, I investigated age-related differences in motor imagery EEG-based neurofeedback (Zich et al., 2015). In line with the HAROLD model, we found reduced lateralisation in older adults during motor imagery. This finding was replicated in an independent sample across different imaging modalities using simultaneous EEG-fNRIS (Zich et al., 2017). 

 

Related publications:​

  • Zich C, Debener S, Kranczioch C, Bleichner MG, Gutberlet I, De Vos M, 2015

Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery

NeuroImage 114: 438-447, https://doi.org/10.1016/j.neuroimage.2015.04.020

  • Zich C, Debener S, De Vos M, Frerichs S, Maurer S, Kranczioch C, 2015

Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study

NeuroImage 116: 80-91, https://doi.org/10.1016/j.neuroimage.2015.05.009​​

  • Zich C, Debener S, Thoene AK, Chen LC, Kranczioch C, 2017

Simultaneous EEG-fNIRS reveals age-related changes in cortical signatures of motor imagery neurofeedback

Neurobiol Aging, 49, 183-197, https://doi.org/10.1016/j.neurobiolaging.2016.10.011​​

​

Mobile EEG 

EEG-based neurofeedback uniquely allows frequent training outside a laboratory setting, as mobile solutions exist. I validated mobile EEG for motor imagery neurofeedback, by comparing a laboratory state-of-the-art EEG system with a low-cost, mobile EEG that was administered in daily life (Zich et al., 2015). I used mobile EEG to administer frequent training with chronic stroke survivors at their homes and evaluated the training using multimodal neuroimaging (Zich et al., 2017). 

 

Related publications:

  • Kranczioch C, Zich C, Schierholz I, Sterr A, 2014

Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation

Int J Psychophysiol 91(1): 10-15, https://doi.org/10.1016/j.ijpsycho.2013.10.004

  • Zich C, De Vos M, Kranczioch C, Debener S, 2015

Wireless EEG with individualized channel layout enables efficient motor imagery training

Clin Neurophysiol 126(4): 698-710, https://doi.org/10.1016/j.clinph.2014.07.007​

  • Zich C, Debener S, Schweinitz C, Sterr A, Meekes J, Kranczioch C, 2017

High intensity chronic stroke motor imagery neurofeedback training at home – three case reports

Clin EEG Neurosci 48 (6): 403-412, https://doi.org/10.1177/1550059417717398​

  • Braun N, Kranczioch C, Liepert J, Dettmers C, Zich C, Büsching I, Debener S, 2017

Motor imagery impairment in post-acute stroke patients

Neural Plast 2017 (4653256), https://doi.org/10.1155/2017/4653256 

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Context factors

Currently, I am collaborating with Dr Cornelia Kranczioch and Mareike Daeglau on the DFG grant ‘Optimising the context of motor imagery neurofeedback training’. Specifically, we were investigating the influence of a) a competitive multi-user scenario (Daeglau et al., 2020), b) prior physical practice (Daeglau et al., 2020), and c) the influence of declarative interference and sleep (Daeglau et al., 2021). We recently summarised the impact of context on EEG motor imagery neurofeedback and related motor domains in a review paper (Daeglau et al., 2021). 

 

Related publications: 

  • Daeglau M, Condro IS, Wallhoff F, Debener S, Zich C, 2020

NAO race: exploring social context on motor imagery performance

Sensors 20(6), 1620: 1-16, https://doi.org/10.3390/s20061620​

  • Daeglau M, Zich C, Emkes R, Welzel J, Debener S, Kranczioch C, 2020

Investigating priming effects of physical practice on motor imagery-induced event-related desynchronization

Front Psychol 11(57): 1-14, https://doi.org/10.3389/fpsyg.2020.00057

  • Daeglau M, Zich C, Welzel J, Saak SK, Scheffels JF, Debener S, Kranczioch C, 2021

Event-related desynchronization in motor imagery with EEG neurofeedback in the context of declarative interference and sleep

NeuroImage: Reports. 1(4), 100058, https://doi.org/10.1016/j.ynirp.2021.100058​

  • Daeglau M, Zich C, Kranczioch C, 2021

Impact of Context on EEG Motor Imagery Neurofeedback and Related Motor Domains

Current Behavioural Neuroscience Reports, 8 90-101, https://doi.org/10.1007/s40473-021-00233-w

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