Head

PD Dr. Thomas Knösche
PD Dr. Thomas Knösche
Group leader
Phone: +49 341 9940-2619
Fax: +49 341 9940-2624

Contact

Nancy Muschall
Assistant Methods and Development Units
Phone: +49 341 9940-2287
Fax: +49 341 9940-2448

MEG and Cortical Networks

Cortical Networks and Cognitive Functions

Introduction

How is it possible to explain the astonishing capabilities of the human brain on the basis of its anatomy and physiology? It has been accepted for a long time that different brain functions are rooted in different brain areas. This segregation principle has been confirmed by numerous studies. For example, it has been shown repeatedly that certain cortical areas seem to be specialized in the visual perception of faces or houses, respectively. However, segregation alone is not sufficient to explain the complexity of brain function. We know that the psychological functions of humans are highly interdependent. For example, actions depend in a complex way on perception, attention and emotional status. Functional integration is therefore the second important functional principle of the brain. Taking a look at the anatomy of the neural system confirms this: neurons and neural populations are indeed interconnected in a very complex way. In order to investigate how the brain is able to perform, it is therefore necessary to study the anatomical connection patterns, to model the interaction between neuronal populations, and to validate these models using observations and measurements. In the following, we will describe how we might be able to achieve these goals based on non-invasive measurements in healthy volunteers.

Tractography - the Reconstruction of the Network of Nerve Fibers

In order to reconstruct the course of nerve fibers and hence the blueprint of the neuronal circuitry, several classical methods are available. For example, the migration of tracer substances along fibers can be observed or the fiber pathways can be reconstructed using series of polarized light micrographs. However, all these methods can only be applied to dead material or in animal models. Therefore, it is difficult to directly investigate the networks underlying the specific cognitive faculties of humans. In recent years, the emergence of diffusion weighted magnetic resonance imaging offered a possibility to in vivo monitor the direction-dependent mobility of water molecules, which allows for inferences on the microstructure of the tissue, in particular on the orientation of nerve fibers. Based on a mathematical modeling technique, called tractography, it is possible to reconstruct the probable courses of nerve fiber tracts [1-7]. This methodology offers a way to image the anatomical connectivity in the living human brain. For example, we were be able to demonstrate separate brain networks for the processing of different types of grammar, investigate the development of the human language network and to explore specific connections between the neural systems dedicated to auditory and visual perception [8-10]. Moreover, one can map differences in particular tissue properties along fiber bundles [2-3], which may be related to certain diseases or cognitive capabilities. Finally, it is possible to compute for each portion of the cortex a specific connectivity profile. Under the premise that the connectivity of a brain structure with the rest of the brain is of great importance for its function, the comparison of these connectivity profiles yields information on the division of the cortex into functional units. This methodology is referred to as functio-anatomical parcellation [11-14]. A more detailed treatment of this issue is found in [13]. Hence, using tractography we are able to elucidate the anatomical aspects of both segregation (by reconstruction of parcellations) and integration (by quantifying the connectivity between brain areas). It is of paramount importance that this can be done in a non-invasive way in normal healthy human subjects, which allows us to connect this information with the specifically human faculties of the brain.

Models of Interacting Neurons

The existence of anatomical connections between particular neural populations does merely reflect the potential for information transfer, not the information transfer itself. In order to investigate, if, how, and to what degree brain areas really interact, mathematical models are used. In order to keep these models tractable and descriptive for the most important properties of the neural tissue at the same time, we use so-called neural mass and neural field models. In these models, many similar neurons are lumped together and are represented jointly by the relationship between their mean input and mean output. An interesting feature of these models is that they can be used to predict measurements, such as electroencephalography, magnetoencephalography and functional magnetic resonance imaging [15-17]. Therefore, they are generative models and it is in principle possible to estimate their parameters, such as connectivity strengths, from measured data [15]. Based on this, one can model stimulation or behavior dependent variation of brain signals at the level of neural populations. This technique is referred to as Dynamic Causal Modeling. For example, the detection of deviants in a uniform stream of stimuli has been explained within a hierarchy of cortical neural populations [18].

Multimodal Modelling

The research in the group "Cortical Networks and Cognitive Functions" is aimed at combination and integration of the dynamic models of neural networks as described above with information on the anatomical connectivity of the brain as obtained by diffusion weighted magnetic resonance imaging, with functional magnetic resonance imaging, with electroencephalographic and magnetoencephalographic recordings, and finally with human perception and behavior. This way, we hope to forge a powerful tool for the investigation of those biological mechanisms that form the foundation for the cognitive faculties of humans.

Literature

[1] T.R. Knösche, A. Anwander, M. Liptrot, T.B. Dyrby: Validation of tractography – comparison with manganese tracing. Human Brain Mapping 36(10):4116-34 (2015)

[2] T. Riffert, J. Schreiber, A. Anwander, T.R. Knösche: Beyond fractional anisotropy: extraction of bundle-specific structural metrics from crossing fiber models. NeuroImage 100, 176–191 (2014)

[3] J. Schreiber, T. Riffert, A. Anwander, T.R. Knösche: Plausibility Tracking: A method to evaluate anatomical connectivity and microstructural properties along fiber pathways.  NeuroImage 90, 163-178 (2014)

[4] D.K. Jones, T.R. Knösche, R. Turner: White matter integrity, fiber count, and other fallacies: the do’s and don’t’s of diffusion MRI. NeuroImage 73, 239-54 (2013)

[5] R.M. Heidemann, A. Anwander, T. Feiweier, T.R. Knösche, R. Turner: k-space and q-space: Combining ultrahigh spatial and angular resolution in diffusion imaging using ZOOPPA at 7T. NeuroImage 60(2), 967-978 (2012)  

[6] M. Descoteaux, R. Deriche, T.R. Knösche, A. Anwander: Deterministic and probabilistic tractography based on complex fiber orientation distributions, IEEE Transactions on Medical Imaging 28, 269-286 (2009)

[7] E. Kaden, T.R. Knösche, A. Anwander: Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging, NeuroImage 37, 474-488 (2007)

[8] A.D. Friederici, J. Bahlmann, S. Heim, R.I. Schubotz, A. Anwander: The brain differentiates human and non-human grammars: Functional localization and structural connectivity. Proceedings of the National Academy of Sciences of the United States of America 103(7), 2458-2463 (2006)

[9] J. Brauer, A. Anwander, A. D. Friederici: Neuroanatomical prerequisites for language functions in the maturing brain. Cerebral Cortex 21(2), 459-466 (2011)

[10] H. Blank, A. Anwander, K. von Kriegstein, K. (2011). Direct structural connections between voice and face-recognition areas. The Journal of Neuroscience 31(36), 12906-12915 (2011)

[11] D. Moreno-Dominguez, A. Anwander, T.R. Knösche: A Hierarchical Method for Whole-Brain Connectivity-Based Parcellation. Human Brain Mapping 35, 5000–5025 (2014)

[12] M. Ruschel, T.R. Knösche, A.D. Friederici, R. Turner, S. Geyer, A. Anwander: Connectivity architecture and subdivision of the human inferior parietal cortex revealed by diffusion MRI. Cerebral Cortex 24(9): 2436-2448 (2014)

[13] T.R. Knösche and M. Tittgemeyer: The role of long-lange connectivity for the characterization of the functional-anatomical organization of the cortex, Frontiers in System Neuroscience 5:58. (Epub 2011)

[14] A. Anwander, M. Tittgemeyer, A.D. Friederici, D.Y. von Cramon, T.R. Knösche: Connectivity-based cortex parcellation of Broca’s area, Cerebral Cortex 17(4), 816-825 (2007)

[15] P. Wang, T.R. Knösche: A realistic neural mass model of the cortex with laminar-specific connections and synaptic plasticity – evaluation with auditory habituation, PLoS ONE 8(10) e77876 (2013)

[16] A. Spiegler, T.R. Knösche, K. Schwab, J. Haueisen, F.M. Atay: Modeling brain resonance phenomena using a neural mass model, PLoS Computational Biology 7(12) (2011)

[17] A. Spiegler, S. Kiebel, F. Atay, T.R. Knösche: Bifurcation analysis of neural mass models: impact of extrinsic inputs and dendritic time constants. NeuroImage 52(3), 1041-1058 (2010)

[18] M.I. Garrido, K.J. Friston, S.J. Kiebel, K.E. Stephan, T. Baldeweg, J.M. Kilner: The functional anatomy of the MMN: A DCM study of the roving paradigm. NeuroImage 42 (2), 936-944 (2008)

 
Go to Editor View
loading content