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Contact Information

Contact

Postal Address

City, University of London
Northampton Square
London
EC1V 0HB
United Kingdom

About

Overview

Dr Allefeld is a cognitive neuroscientist with a background in physics and philosophy.

His research is about developing and improving statistical models and data analysis methods for neuroimaging (EEG, fMRI) and behavioural data. His work tries to make concepts of physics, nonlinear dynamics, systems theory, and information theory fruitful for cognitive neuroscience and psychology. He is particularly interested in the relation between neural and mental states.

Qualifications

  1. Dr. rer. nat., University of Potsdam, Germany, Nov 2000 – Nov 2004

Employment

  1. Lecturer, City, University of London, Sep 2019 – present
  2. Research Associate, Bernstein Center for Computational Neuroscience Berlin, Nov 2009 – Dec 2018

Languages

English (can read, write, speak, understand spoken and peer review) and German (can read, write, speak, understand spoken and peer review).

Publications

  1. Görgen, K., Hebart, M.N., Allefeld, C. and Haynes, J.D. (2018). The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods. NeuroImage, 180, pp. 19–30. doi:10.1016/j.neuroimage.2017.12.083.
  2. Allefeld, C., Görgen, K. and Haynes, J.D. (2016). Valid population inference for information-based imaging: From the second-level t-test to prevalence inference. NeuroImage, 141, pp. 378–392. doi:10.1016/j.neuroimage.2016.07.040.
  3. Soch, J., Haynes, J.D. and Allefeld, C. (2016). How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection. NeuroImage, 141, pp. 469–489. doi:10.1016/j.neuroimage.2016.07.047.
  4. Allefeld, C. and Haynes, J.D. (2014). Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA. NeuroImage, 89, pp. 345–357. doi:10.1016/j.neuroimage.2013.11.043.
  5. Allefeld, C., Atmanspacher, H. and Wackermann, J. (2009). Mental states as macrostates emerging from brain electrical dynamics. Chaos, 19(1). doi:10.1063/1.3072788.

Chapters (3)

  1. Kuhlen, A.K., Allefeld, C., Anders, S. and Haynes, J.D. (2015). Towards a multi-brain perspective on communication in dialogue. Cognitive Neuroscience of Natural Language Use (pp. 182–200). ISBN 978-1-107-04201-8.
  2. Allefeld, C. and J-D Haynes, (2015). Multi-voxel Pattern Analysis. Brain Mapping: An Encyclopedic Reference (pp. 641–646). ISBN 978-0-12-397316-0.
  3. Wackermann, J. and Allefeld, C. (2009). State space representation and global descriptors of brain electrical activity. Electrical Neuroimaging (pp. 191–214). ISBN 978-0-521-87979-8.

Conference papers and proceedings (3)

  1. Allefeld, C. (2005). Phase vs. amplitude correlations in event-related potentials.
  2. Allefeld, C. and Frisch, S. (2004). Phase synchronization analysis of event-related potentials in language processing.
  3. Allefeld, C. and Kurths, J. (2003). Multivariate Phase Synchronization Analysis of EEG Data.

Journal articles (33)

  1. Soch, J. and Allefeld, C. (2018). MACS – a new SPM toolbox for model assessment, comparison and selection. Journal of Neuroscience Methods, 306, pp. 19–31. doi:10.1016/j.jneumeth.2018.05.017.
  2. Christophel, T.B., Allefeld, C., Endisch, C. and Haynes, J.D. (2018). View-Independent Working Memory Representations of Artificial Shapes in Prefrontal and Posterior Regions of the Human Brain. Cerebral cortex (New York, N.Y. : 1991), 28(6), pp. 2146–2161. doi:10.1093/cercor/bhx119.
  3. Christophel, T.B., Iamshchinina, P., Yan, C., Allefeld, C. and Haynes, J.D. (2018). Cortical specialization for attended versus unattended working memory. Nature Neuroscience, 21(4), pp. 494–496. doi:10.1038/s41593-018-0094-4.
  4. Soch, J., Meyer, A.P., Haynes, J.D. and Allefeld, C. (2017). How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging. NeuroImage, 158, pp. 186–195. doi:10.1016/j.neuroimage.2017.06.056.
  5. Schultze-Kraft, M., Birman, D., Rusconi, M., Allefeld, C., Görgen, K., Dähne, S. … Haynes, J.D. (2016). The point of no return in vetoing self-initiated movements. Proceedings of the National Academy of Sciences of the United States of America, 113(4), pp. 1080–1085. doi:10.1073/pnas.1513569112.
  6. Ritter, K., Schumacher, J., Weygandt, M., Buchert, R., Allefeld, C. and Haynes, J.D. (2015). Multimodal prediction of conversion to Alzheimer's disease based onincomplete biomarkers. Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, 1(2), pp. 206–215. doi:10.1016/j.dadm.2015.01.006.
  7. Weygandt, M., Hummel, H.M., Schregel, K., Ritter, K., Allefeld, C., Dommes, E. … Gärtner, J. (2015). MRI-based diagnostic biomarkers for early onset pediatric multiple sclerosis. NeuroImage: Clinical, 7, pp. 400–408. doi:10.1016/j.nicl.2014.06.015.
  8. Ramírez, F.M., Cichy, R.M., Allefeld, C. and Haynes, J.D. (2014). The neural code for face orientation in the human fusiform face area. Journal of Neuroscience, 34(36), pp. 12155–12167. doi:10.1523/JNEUROSCI.3156-13.2014.
  9. Rusconi, M., Allefeld, C., Hohlefeld, F.U., Deutschlaender, R., Christophel, T.B., Neumann, W.-.J. … Haynes, J.-.D. (2014). LP25: Predicting “free” motor-decisions from subcortical brain signals. Clinical Neurophysiology, 125. doi:10.1016/s1388-2457(14)50494-4.
  10. Soon, C.S., Allefeld, C., Bogler, C., Heinzle, J. and Haynes, J.D. (2014). Predictive brain signals best predict upcoming and not previous choices. Frontiers in Psychology, 5(MAY). doi:10.3389/fpsyg.2014.00406.
  11. Winkler, I., Brandl, S., Horn, F., Waldburger, E., Allefeld, C. and Tangermann, M. (2014). Robust artifactual independent component classification for BCI practitioners. Journal of Neural Engineering, 11(3). doi:10.1088/1741-2560/11/3/035013.
  12. Kuhlen, A.K., Allefeld, C. and Haynes, J.D. (2012). Content-specific coordination of listeners' to speakers' EEG during communication. Frontiers in Human Neuroscience, (SEPTEMBER). doi:10.3389/fnhum.2012.00266.
  13. Ramirez, F., Cichy, R.M., Allefeld, C. and Haynes, J.-.D. (2012). Translation tolerant and category-selective encoding of orientation in the fusiform face area. Journal of Vision, 12(9), pp. 1180–1180. doi:10.1167/12.9.1180.
  14. Hackmack, K., Paul, F., Weygandt, M., Allefeld, C. and Haynes, J.D. (2012). Multi-scale classification of disease using structural MRI and wavelet transform. NeuroImage, 62(1), pp. 48–58. doi:10.1016/j.neuroimage.2012.05.022.
  15. Heinzle, J., Allefeld, C. and Haynes, J.D. (2012). Information flow, dynamical systems theory and the human brain. Comment on "Information flow dynamics in the brain" by M.I. Rabinovich et al. Physics of Life Reviews, 9(1), pp. 78–79. doi:10.1016/j.plrev.2011.12.007.
  16. Allefeld, C., Pütz, P., Kastner, K. and Wackermann, J. (2011). Flicker-light induced visual phenomena: Frequency dependence and specificity of whole percepts and percept features. Consciousness and Cognition, 20(4), pp. 1344–1362. doi:10.1016/j.concog.2010.10.026.
  17. Ott, U., Wackermann, J., Allefeld, C., Gebhardt, H., Walter, B. and Vaitl, D. (2010). Global EEG descriptors and default-mode network during daydreaming and meditation. International Journal of Psychophysiology, 77(3), pp. 217–217. doi:10.1016/j.ijpsycho.2010.06.299.
  18. Bialonski, S., Allefeld, C., Wellmer, J., Elger, C. and Lehnertz, K. (2009). 87. An approach to identify synchronization clusters within the epileptic network. Clinical Neurophysiology, 120(1). doi:10.1016/j.clinph.2008.07.086.
  19. Allefeld, C. (2008). The hollow of being: What can we learn from merleau-ponty's ontology for a science of consciousness? Mind and Matter, 6(2), pp. 235–255.
  20. Bialonski, S., Allefeld, C., Wellmer, J., Elger, C. and Lehnertz, K. (2008). An approach to identify synchronization clusters within the epileptic network. Klinische Neurophysiologie, 39(01). doi:10.1055/s-2008-1072881.
  21. Wackermann, J., Pütz, P. and Allefeld, C. (2008). Ganzfeld-induced hallucinatory experience, its phenomenology and cerebral electrophysiology. Cortex, 44(10), pp. 1364–1378. doi:10.1016/j.cortex.2007.05.003.
  22. Allefeld, C. and Bialonski, S. (2007). Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 76(6). doi:10.1103/PhysRevE.76.066207.
  23. Wackermann, J. and Allefeld, C. (2007). On the meaning and interpretation of global descriptors of brain electrical activity. Including a reply to X. Pei et al. International Journal of Psychophysiology, 64(2), pp. 199–210. doi:10.1016/j.ijpsycho.2007.02.003.
  24. Allefeld, C., Müller, M. and Kurths, J. (2007). Eigenvalue decomposition as a generalized synchronization cluster analysis. International Journal of Bifurcation and Chaos, 17(10), pp. 3493–3497. doi:10.1142/S0218127407019251.
  25. Allefeld, C. (2006). About the derivation of the SCA algorithm. International Journal of Bifurcation and Chaos, 16(12), pp. 3705–3706. doi:10.1142/S0218127406017099.
  26. Allefeld, C., Frisch, S. and Schlesewsky, M. (2005). Detection of early cognitive processing by event-related phase synchronization analysis. NeuroReport, 16(1), pp. 13–16. doi:10.1097/00001756-200501190-00004.
  27. Allefeld, C. and Kurths, J. (2004). Testing for phase synchronization. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 14(2), pp. 405–416. doi:10.1142/S021812740400951X.
  28. Allefeld, C. and Kurths, J. (2004). An approach to multivariate phase synchronization analysis and its application to event-related potentials. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 14(2), pp. 417–426. doi:10.1142/S0218127404009521.
  29. Soch, J. and Allefeld, C. Kullback-Leibler Divergence for the Normal-Gamma Distribution. .
  30. Soch, J. and Allefeld, C. Exceedance Probabilities for the Dirichlet Distribution. .
  31. Allefeld, C. Instantaneous oscillatory direction and phase for multivariate
    timeseries.
    .
  32. Allefeld, C., Soon, C.S., Bogler, C., Heinzle, J. and Haynes, J.-.D. Sequential dependencies between trials in free choice tasks. .
  33. Soch, J., Allefeld, C. and Haynes, J.-.D. Inverse Transformed Encoding Models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding. . doi:10.1101/610626.