Computational Neuroimaging

 

Research Activities

The ”Computational Neuroimaging” (CN) group is headed by Kristoffer H. Madsen. The main focus of the group is modelling and analysis of neuroimaging data based on machine learning methodology. The efforts within the group aim to improve sensitivity and interpretability of the vast amounts of data that are acquired with neuroimaging techniques through sophisticated modelling and analysis methods. Furthermore, the group makes significant contributions to a wide range of projects running within DRCMR.

The CN group maintains particularly close collaboration with the “Neurophysics” group headed by Axel Thielscher, the “Biophysically Adjusted State-Informed Cortex Stimulation (BaSiCs)” group headed by Hartwig R. Siebner, and the “Reward and Homeostasis” group headed by Oliver Hulme.


Key Research Areas

Modelling of functional connectivity

By continuous observation of brain activity, connectivity between brain areas can be inferred through the identification of statistical dependencies between signal from distinct brain areas. The research efforts within this area are mainly focused on unsupervised multivariate modelling CMA techniques for functional magnetic resonance imaging data (fMRI). Whereas traditional analyses of fMRI data typically involve inferring brain activity for each volume element (voxel) individually using a so-called mass-univariate approach. In contrast unsupervised multivariate modelling techniques aims to identify latent networks of functional connectivity by simultaneously considering all voxels in the brain in a combined multivariate model. Current efforts within this area are on multi-way extensions of unsupervised decomposition models, such as the well-known Independent Component Analysis, to naturally extend modelling of data recorded over several trials, sessions, subjects and modalities. One example is a Bayesian formulation of Independent Vector Analysis for modelling of functional networks across subjects in the presence of limited inter-subject variability. Additional research efforts are within Bayesian nonparametric modelling of fMRI data and investigations of predictability and reproducibility of unsupervised decomposition methods using resampling techniques.

 

Source localization for EEG/MEG

EEG and MEG data records electrical and magnetic electrophysiological signals respectively over a limited number of electrodes or sensors (in the order of hundreds) outside the skull. The so-called source localization problem is concerned with identifying the origin/source of these signals inside the brain. This ill-posed problem is challenging mainly because the conductive properties of the head renders the signals measured outside the skull very correlated, and because the number of sensors available are typically much less that the potential number of locations for the sources. In this research we aim to investigate how the quality of the head model, accuracy of the conductive properties of the head and the source localization model type and regularization. This is approached through rigorous biophysical head modelling in collaboration with the neurophysics group and functional multimodal validation experiments involving fMRI and simultaneous EEG/MEG of well established behavioral paradigms.

Selected Publications

Cai, X-L., Xie, D-J., Madsen, K. H., Wang, Y-M., Bögemann, S. A., Cheung, E. F. C., Møller, A. & Chan, R. C. K.
Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.
Human Brain Mapping. 41, 1, p. 172-184, 2020.

Wang, Y-M., Yang, Z-Y., Cai, X-L., Zhou, H-Y., Zhang, R-T., Yang, H-X., Liang, Y-S., Zhu, X-Z., Madsen, K. H., Sørensen, T. A., Møller, A., Wang, Z., Cheung, E. F. C. & Chan, R. C. K.
Identifying Schizo-Obsessive Comorbidity by Tract-Based Spatial Statistics and Probabilistic Tractography.
Schizophrenia Bulletin. p. 1-12.

Hansen, S. T., Hemakom, A., Gylling Safeldt, M., Krohne, L. K., Madsen, K. H., Siebner, H. R., Mandic, D. P. & Hansen, L. K.
Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition.
Computational Intelligence and Neuroscience. 2019, p. 1-15, 5618303. 2019.

Krohne, L. G., Wang, Y., Hinrich, J. L., Moerup, M., Chan, R. C. K. & Madsen, K. H.
Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task.
Human Brain Mapping. 40, 17, p. 4965-4981, 2019.

Madsen, K. H., Karabanov, A. N., Krohne, L. G., Safeldt, M. G., Tomasevic, L. & Siebner, H. R.
No trace of phase: Corticomotor excitability is not tuned by phase of pericentral mu-rhythm.
Brain Stimulation. 12, 5, p. 1261-1270, 2019.

Nielsen, S. F. V., Madsen, K. H., Vinberg, M., Kessing, L. V., Siebner, H. R. & Miskowiak, K. W.
Whole-Brain Exploratory Analysis of Functional Task Response Following Erythropoietin Treatment in Mood Disorders: A Supervised Machine Learning Approach.
Frontiers in Neuroscience. 13, p. 1-11, 1246. 2019.

Saturnino, G. B., Madsen, K. H. & Thielscher, A.
Electric field simulations for transcranial brain stimulation using FEM: an efficient implementation and error analysis.
Journal of Neural Engineering. 16, 6, 27 p., 066032. 2019.

Saturnino, G. B., Siebner, H. R., Thielscher, A. & Madsen, K. H.
Accessibility of cortical regions to focal TES: Dependence on spatial position, safety, and practical constraints.
NeuroImage. 203, p. 1-17, 116183. 2019.

Wang, Y-M., Cai, X-L., Zhang, R-T., Wang, Y., Madsen, K. H., Sørensen, T. A., Møller, A., Cheung, E. F. C. & Chan, R. C. K.
Searchlight classification based on Amplitude of Low Frequency Fluctuation and functional connectivity in individuals with obsessive-compulsive symptoms.
Cognitive neuropsychiatry. 24, 5, p. 322-334, 2019.

Madsen KH, Krohne LG, Cai X, Wang Y, Chan, RCK (2018). “Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data”, Schizophrenia Bulletin.

Nielsen SFV, Schmidt MN, Madsen KH, Mørup M, (2018), “Predictive assessment of models for dynamic functional connectivity”. Neuroimage

Hinrich, JL, Nielsen, SFV, Madsen, KH, Morup, M (2018). Variational Bayesian Partially Observed Non-Negative Tensor Factorization. In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–6). IEEE

Nielsen, SFV, Levin-Schwartz, Y, Vidaurre, D, Adali, T, Calhoun, VD, Madsen, KH., Hansen LK, Morup, M (2018). Evaluating Models of Dynamic Functional Connectivity Using Predictive Classification Accuracy. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2566–2570). IEEE.

Nielsen, SFV, Vidaurre, D, Madsen, KH, Schmidt, MN, Morup, M (2018). Testing group differences in state transition structure of dynamic functional connectivity models. In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI) (pp. 1–4). IEEE.

Group Members

Kristoffer Hougaard Madsen

Group Leader

Oliver Hulme

Line Korsgaard Johnsen

External Collaborators

Morten Mørup

Section for Cognitive System, DTU Compute


Prof. Lars Kai Hansen

Section for Cognitive System, DTU Compute


Prof. Fang Wang

Institute of Biophysics, Chinese Academy of Science


Prof. Rong Xue

Institute of Biophysics, Chinese Academy of Sciences


Nathan W. Churchill

Department of Medical Biophysics, University of Toronto, Canada


Prof. Raymond Chan

Institute of Physchology, Chinese Academy of Sciences