In this project, novel technologies and analysis methods for ultra-high field magnetic resonance imaging will be developed. The overall goal here is to develop tools and methods that allow modeling of the human cortex in three dimensions, that is, perpendicular to the cortical surface (dimensions 1 and 2) and in cortical depth (dimension 3), in order to answer research questions about neural resources literally in previously undiscovered dimensions.
Prof. Dr. Michael Hanke
Michael Hanke is the head of the Psychoinformatics team at the Institute of Neuroscience and Medicine – Brain and Behaviour (INM-7) at the Jülich Research Centre, where he has developed research software and computational methodology for machine learning-based analysis and functional alignment of neuroimaging data. He will assist with the technical implementation of computational pipelines and their dissemination.
Dr. Esther Kühn
Esther Kühn is head of the Research Group “Cortical Microstructure in Health and Disease” at the Institute for Cognitive Neurology and Dementia Research at the Medical Faculty. She investigates adaptive and maladaptive changes in the cortical microstructure of the human sensorimotor system that she investigates using ultra-high field magnetic resonance imaging in combination with behavioral assessments. Her research aim is to understand and treat maladaptive brain states of the sensorimotor system, in particular those in advanced age, in mental illness, and in conditions of motor disorders. She contributes to the CRC by her expertise in the areas of mesoscale imaging using 7T-MRI, mesoscale data modelling, and the biological interpretation of MR-based tissue contrast.
Project Title:B04 Effects of hippocampal vascularization patterns on the neural resources of MTL neurocognitive circuits
:Z02 Human imaging at meso-scale
Prof. Dr. Oliver Speck
Oliver Speck is head of the Department of Biomedical Magnetic Resonance at the Institute of Physics of the Faculty of Natural Sciences. He conducts research in the field of ultra-high field MRI and its neuroscientific applications. A particular aim is to develop methods for high-resolution in vivo imaging of the human brain. This is achieved through fast imaging techniques, methods for prospective correction of movements of the head and methods for geometrically correct imaging of brain structures. He supports the CRC with his expertise in MRI methodology, MRI hardware and application in neuroscience.
Project Title:Z02 Human imaging at meso-scale
I completed my M.Tech in Signal Processing (Indian Institute of Technology, Indore), and worked as a Research and Development engineer at the National Brain Research Centre, India, where I gained experience in the field of Neuro-imaging and spectroscopy using a 3T-MRI machine. Currently, I am using Ultra-High-resolution fMRI data to work on a new method for data aggregation and decoding strategies. I am also working on layer fMRI data analysis and plan to do so using VASO sequence at a 7T-MRI machine to understand the fundamental mechanisms of human motor control using data glove systems that allow real-time motor movement tracking.
Dr. Falk Lüsebrink
Non-invasive imaging provides revolutionary insights
MRI imaging has revolutionized neuroscience over the past 20 years by providing information about brain anatomy, function and connectivity without invasive procedures. By using higher magnetic field strengths, such as the 7 Tesla MRI, finer image resolution can be achieved, allowing greater sensitivity for detecting small differences in activation or structure in the sub-millimeter range. These developments allow, for example, the detailed detection and quantification of anatomical substructures within the cortex (Lüsebrink et al. 2013, 2017, 2021), within brain areas relevant for memory and neurodegeneration such as the hippocampus (Berron et al. 2017), and in the sensory cortices important for the processing of feelings (Kuehn et al. 2017). In addition, the description of small functional units, such as detailed retinotopic map (Hoffmann et al. 2009), somatotopic maps (Kuehn et al. 2018) improves, as well as the dissociation between so-called ‘feedforward’ and ‘feedback’ processing (Kok et al. 2016, Muckli et al. 2015) possible via the detection of activation profiles within cortical columns (Chaimow et al. 2018). All of these techniques provide detailed insights into cognitive processing structures not previously possible with standard analysis techniques.
What is the cortex of the brain?
The outer layer of the brain is the cerebral cortex. It is composed of about 100 billion nerve cells and is called grey matter. Due to its typical folding, it takes up about half of the brain volume. Already at the beginning of the 20th century, the cortex was divided into six layers according to its cell architecture, and due to differences in this architecture into different functional regions. Various studies have been able to demonstrate a correlation between, for example, neurological diseases such as Alzheimer’s and a decrease in the thickness of the cortex in certain functional regions, which can be directly linked to symptoms of the disease, such as memory problems. This results in a direct correlation between the spatial expression of a functional region and the quality of its function.
Tasks in the Collaborative Research Center
The project Z02 plays an important role as a central project in the Collaborative Research Center, as we support the other projects in challenges of image acquisition and analysis using 7 Tesla magnetic resonance imaging (7 T-MRI), and by pushing forward the limits of novel technologies ourselves. Current research on neural resources in humans particularly benefits from novel technology and/or methodology required to describe neural changes at the mesoscale (i.e. less than 1 mm). This benefits the transfer of knowledge from animal research findings (described at the micro level) to brain models and interventions in humans. To this end, we are establishing state-of-the-art MR sequences that provide reproducible and optimized data quality, and computational tools and analysis pipelines for multimodal and multiscale data modelling within and between individuals. In particular, these new methods extend the previous view of the human cortex as a folded surface to consider the depth within the cortex, allowing its different layers to be taken into account in data analysis. As a result, research questions about neural resources can literally be answered in previously undiscovered dimensions.
Artifacts by high field strengths
However, one challenge to successful imaging at the mesoscale of the living human brain is the high sensitivity of these methods to image degradation effects. Indeed, the number and magnitude of image artefacts increase dramatically at field strengths of 7 T compared to 1.5 T and 3 T MRI data (Ladd et al. 2018). Many of these artefacts are caused by subject motion and limit the actual effective image resolution and image quality that can be achieved. In functional MRI (fMRI), brain areas are specifically activated by so-called stimuli. These stimuli can for example be sounds or images. If the participant’s movement correlates with the presentation of the stimulus, the movement can falsify the measurement of brain activations. In addition, the test participant him-/herself is a source of interference that leads to distortions of the strong magnetic field. Although these distortions in themselves contain information (for example used in quantitative susceptibility mapping, QSM), they cause geometric distortions in the image that can affect the analysis or spatial overlay between different data sets. All these effects make signal processing and artefact correction a significant and indispensable area of research in ultra-high field imaging of the brain, especially when submillimeter resolution is the goal. It is therefore a central aspect of Z02 to develop new methods for artefact and motion correction and make them available to researchers in the Collaborative Research Center.
Challenges posed in data analysis
Another challenge for successful mesoscale imaging of the living human brain is the availability of computational tools and algorithms to model fine-grained brain structures and brain functions, such as cortical layers, in different MRI contrasts and their interaction (Kemper et al. 2018, Edwards et al. 2018). This is non-trivial as cortical layers are tiny and not directly visible in in vivo MRI data due to the lack of contrast. Furthermore, different MRI contrasts are often acquired at different image resolutions and viewing angles and analyzed in different software packages sometimes even requiring different computer systems. Interactions and structure-function relationships on the mesoscale can therefore not be readily determined. Z02 focuses on the optimization and further development of automated analysis pipelines that will enable researchers in the Collaborative Research Center to produce reproducible, observer-independent and generalizable research results. We have used different pipelines and toolkits in previous projects to create mesoscale segmentations on ultra-high resolution MR data and combine them with functional data. In addition, we have used novel structural MRI sequences, such as submillimeter quantitative T1 maps, to investigate the relationship between layer-specific cortex structure and the large-scale organization of functional networks (Kuehn et al. 2017). We optimize, complement and validate these methods as part of the Z02 project and make them accessible to researchers in the Collaborative Research Center.
Matching data between individuals
Another major challenge for successful interpretation of mesoscale data is the need to overlay functional and structural images across different individuals. Group statistics are important to investigate the generalizability of effects across individuals and to test for statistically significant group effects, such as comparing older adults with SuperAgers (people whose brain does not seem to age) – a central goal of the Collaborative Research Center. To date, there are very few computational tools or methods optimized for inter-individual matching of mesoscale brain imaging data. In cases where functional areas do not match anatomical landmarks, discrepancies occur. Any small deviation in the location of activation maps or anatomical structures (e.g. Kuehn et al. 2017) can have a massive impact on the outcome of layer-specific analyses, increasing the need for an exact matching procedure. Furthermore, especially in the clinical context, the amount of imaging data that can be obtained from an individual is limited due to procedural challenges, costs and limited compliance. It is therefore the aim of Z02 to enable adequate inter-individual matching through valid data aggregation across individuals, and to make these methods available to other researchers in the Collaborative Research Center.