Main responsible: Claus Svarer
The research focus of the data analysis group is development and optimization of sophisticated data analysis methods for PET, MRI and SPECT images of the brain.
The group is involved in several projects:
- Optimization of processing pipelines for analysis of molecular PET, and structural and functional MR imaging datasets
- Creation of high resolution atlases of the serotonin receptor binding from high quality PET and MR datasets
- Uni- and multivariate analysis of imaging data using external co-variates like outcome variables from neuropsychology testing, personality measures, performance measures, and blood and gene measurements
- Optimization of PET acquisition protocols
- Developments of methods for precise quantification of PET imaging data using kinetic modelling
- Development of automatic methods for precise delineation of volumes of interest (VOI's) in human and animal datasets
- Optimization of the clinical use of the SPECT scanner.
Quantification methods and variability of the translocator protein SPECT ligand: [123I]CLINDE:
Responsible: Ling Feng
To better understand the role of neuroinflammation has become a prominent topic in neuroimaging due to its clinical implications to assist diagnostic and therapeutic of neuroinflammatory and neurodegenerative diseases. At NRU we are investigating a new imaging biomarker for inflammatory changes: [123I]CLINDE using SPECT scanning. We have demonstrated the capability of this tracer in revealing a strong translocator protein expression in patients with stroke, malignant brain tumors and autoimmune encephalitis. Currently we are (a) evaluating the variability of this tracer both in blood (Figure 1) and in brain within a healthy cohort, and (b) investigating the arterio-venous differences and the feasibility of replacing the invasive arterial blood sampling by population-based approaches.
Figure 1: Plasma/Whole blood ratio as a function of time in different genetic groups: High Affinity Binders (HAB), Mixed Affinity Binders (MAB), and Low Affinity Binders (LAB).
Creation of a high resolution human PET atlas of the serotonin binding in the human brain:
Responsible: Vincent Beliveau
The serotonin (5-HT) system is highly diverse with 7 families of receptors (5HT1 to 5HT7), including 14 subtypes, and a transporter. Serotonin is implicated in a myriad of brain functions and dysfunction of this system is linked to many disorders. Over the years and especially as part of the work done in the Center for Integrated Molecular and Brain Imaging (Cimbi), we have at NRU extensively studied the 5-HT system and has accumulated a rich and unique database of healthy subjects including high resolution structural MRI and PET images targeting the receptors 5-HT1A, 5-HT1B, 5-HT2A (agonist and antagonist) and 5-HT4, and the transporter 5-HTT. These receptors and the transporter represent the major components of the 5-HT system that can be studied with PET neuroimaging. In this project we have by means of a novel pipeline for surface-based analysis developed within FreeSurfer generated a high resolution atlas (Figure 2) of the cerebral 5-HT receptors and transporter distribution in a healthy population, to be offered to the scientific community.
Figure 2: Average volume BPND and BPP maps in MNI152 space, horizontal (top) and sagittal (bottom) view.
Multivariate analysis of PET data in Seasonal Affective Disorder:
Responsible: Martin Nørgaard
Seasonal Affective Disorder (SAD) is a severe season-specific type of depression, affecting up to 5% of Copenhagen inhabitants during wintertime. In the human brain, SAD manifests itself as a mental condition characterized by a phasic occurrence of major depression during wintertime and with subsequent remission in summertime. It is, in part, hypothesized to be triggered by a seasonal misregulation of the serotonin transporter (SERT), the mechanism in which endogenous serotonin (5-HT) is inactivated and recycled into the presynaptic terminal. SERT-levels can be measured with PET imaging and modeled using the 2-step multilinear reference tissue model (MRTM2). However, in neuroimaging data processing choices influence the achieved results and their interpretation heavily. Furthermore, the statistical challenges associated with analyzing neuroimaging data are far from solved, and the adaption of advanced statistical strategies to neuroimaging data constitutes a significant problem. The aim of this project was therefore to employ a variety of statistical and computational analyses (Figure 3) to allow for inference on the complex SERT modulation underpinning SAD.
Figure 3: Flowchart depicting a common pipeline in neuroimaging (PET), showing the causal relationship between data acquisition, preprocessing, data analysis, interpretation and finally development of new hypotheses.
Exploring a multimodal serotonin atlas with the help of machine learning methods:
Responsible: Melanie Ganz
In recent years, there has been an increasing interest towards applying multivariate machine learning techniques to the analysis of neuroimaging data. Determining disease-related variations of the anatomy and its function is an important step in better understanding diseases and developing early diagnostic systems. In particular, image-based multivariate prediction models and the “relevant features” they produce are attracting attention from the community (Figure 4).
Therefore, image-based prediction models hold great promise for improving clinical practice. The ability to understand biological systems as well as predict the state of a disease based on its anatomical and functional signatures opens up new avenues for early diagnostic systems. Comparing images of healthy controls and patients can highlight such variations on a macroscale that would be difficult to identify using histopathology. This is essential for improving our understanding of disease and refining the predictive power of machine learning methods.
Figure 4: (a) Results of a univariate two-tailed t-test of patients having a smaller cortical thickness than controls. This needs to be corrected for multiple comparisons. (b) Results of a cluster-wise multiple comparison correction as done with FreeSurfer and a voxel-wise threshold of 0.01 and a cluster wise threshold of 0.01. Only two regions, the insula and the temporal cortex, survive. On the contrary, a multivariate feature selection shown in (c) reliably identifies additional regions that were found in the intersection of feature selections performed over 5 independent subsets. This case demonstrates how the multivariate analysis can add information to the more common univariate analysis.
Effects of sleep deprivation and adrenergic inhibition on glymphatic flow in humans:
Responsible: Sebastian Holst
Sleep is a universal biological process critical to maintenance of life. Lack of or insufficient sleep, has been associated with a range of diseases including obesity, cardiovascular disease, reduced cognition, impaired learning, and increased risk of motor vehicle accidents. Although advances in our understanding for why we sleep have been reached in recent years, the underlying biological mechanisms remain poorly understood. The glymphatic system is a novel macroscopic pathway of the central nervous system, so far only described in rodents. This system facilitates the clearance of waste products from neuronal metabolism in the brain, preparing the brain to be awake. Fascinatingly, the glymphatic system is specifically activated during sleep and enhanced by up to 90% when compared to wakefulness. In this project (Figure 5), we aim to demonstrate the existence of the glymphatic system and measure glymphatic flow in the human brain. With newly developed magnetic resonance imaging (MRI) protocols such as ultra-fast magnetic resonance encephalography (MREG), we aim to investigate, and pharmacologically interfere with, the glymphatic system in healthy adult volunteers at various levels of sleep need.
Figure 5: Sleep and its fluctuations across the night (left) will be investigated following a pharmacological intervention and during a 2-hour simultaneous EEG/MRI measurement (right) at different levels of sleep loss. Preliminary data in three individuals are shown (bottom right).
Development of automatic atlas delineation method for analysis of the Danish landrace pig PET scans:
Responsible: Claus Svarer
In most animal studies only the functional data is available and it is common practice to manually delineate regions of interest at the functional datasets and use these regions for e.g. extraction of time activity curves that can be used for quantification of binding from PET datasets. In this project we are working on development of a more objective automatic method for automatic delineation of the regions of interest (Figure 6). In the new method it will both be possible to base the delineation of regions of interest on a structural MRI dataset or directly at a functional PET imaging dataset. An atlas for the Danish landrace pig will be made available.
Figure 6: Illustration of an automatic pig atlas with the upper row showing an "early" flow-like PET image overlaid a structural MR atlas and lower row showing the corresponding atlas labels (simplified labels taken from french pig atlas [Saikall et al, J. Neurosci Meth. 2010]).