This study will be part of a multi-center study, contributing to build the EBRAINS 2.0 database. It will be part of the planned “penta-model or 5M Healthy Connectome”, which will include multimodal within-subject data in healthy controls from simultaneously obtained structural and functional MRI, PET, EEG, and neuropsychological testing, giving the opportunity to tackle the question of intra- and intersubject variability. In total, more than 200 healthy volunteers from Denmark, Germany, Austria, and Italy are participating in the project, divided into different age groups ranging from 20 to 80 years. Approximately 60 participants are from Denmark and the acquired data will be integrated in the EBRAINS atlases. The novelty of our research project lies in the fact that, by studying a large number of individuals, we can gain much deeper insight into how the brain functions on multiple levels and understand what this means for aspects like memory and reactions.
We know that over the lifespan, the brain undergoes structural changes (5). Magnetic Resonance Imaging (MRI) techniques, such as the so-called rsfMRI technique, enable researchers to study brain activity and networks at rest, meaning how the different parts of the brain communicate with each other, including network disruptions in neurological and psychiatric disorders, and the overall functional organization of the brain. MRI studies have identified alterations in brain networks in numerous neurological and psychiatric disorders, including Alzheimer's disease, schizophrenia, autism spectrum disorders, and depression (6, 7). Besides MRI, Positron Emission Tomography (PET) and Electroencephalography (EEG) allows to measure various aspects of the brain, such as its glucose metabolism and the brain’s electrical activity, where such studies have fund baseline alterations in numerous brain disorders (8-13). Besides neuroimaging, neuropsychological tests can be used to assess a person's thinking patterns and other aspects, such as memory and reaction time. These tests provide valuable data on how structural and functional connectivity translates into cognitive performance and behavioral outcomes. By correlating neuropsychological test results with neuroimaging findings, it is possible to gain a greater understanding of the functional implications of brain connectivity - the way different regions of the brain communicate and interact with each other (14). In summary, connectivity studies are crucial for advancing our understanding of the complex networks within the brain and their roles in both health and disease. By examining connectivity, researchers can uncover the underlying mechanisms of brain function, identify biomarkers for various neurological and psychiatric disorders, and develop targeted interventions to improve brain health (15).
An increasing number of multimodal neuroimaging studies have already focused on the assessment of objective, predictive markers for treatment response, yielding promising results and paving the way for personalized medicine approaches (16, 17). The Human Brain Project (HBP) was one of the first European large-scale projects to gather data from different neuroimaging modalities in several countries. By using methods from computing, artificial intelligence, and neuroinformatics, the HBP investigated the brain on different spatial and temporal scales, which so far has led to more than 3.000 publications in the field of neuroscience (18). Based on this success, the EBRAINS and EBRAINS 2.0 projects, funded by the EU, were built on the HBP. EBRAINS is a collaborative European Research Infrastructure designed to advance and accelerate progress in neuroscience and brain health by providing data and state-of-the-art digital research tools. It is an ecosystem where researchers, clinicians and experts from various disciplines and countries converge to explore and analyse brain complexity. The project aims to create a new standard for brain atlases to highlight how the brain's structure, internal networks, function, and connections change throughout life in healthy adults. The overarching goal of EBRAINS 2.0 is to foster a deeper understanding of brain structure and function to facilitate the development of more effective treatments, new drugs, diagnostics, and preventive measures for neuro-psychiatric disorders. Globally, EBRAINS 2.0 will make a strong contribution to the new era of digital neuroscience and foster European leadership in this field (19).
Hypotheses
- A deep learning model synthesizing individualized healthy [18F]FDG-PET images from MRI scans will improve the detection of cerebral abnormalities by providing a more accurate baseline, accounting for variations in brain morphology and enhancing the identification of early neurological and psychiatric disorder markers.
- Performance on specific neuropsychological tests is correlated with the connectivity strength within and between specific resting-state networks. For instance, better memory performance will correlate with higher hippocampal glucose metabolism and stronger connectivity within the default mode network (DMN) while better executive function will correlate with stronger connectivity within the fronto-parietal network (FPN).
- Aging leads to significant changes in the brain's hierarchical functional organization, characterized by psychomotor slowing and increased global metabolic demand, measurable through behavioral tests and PET imaging.
Primary Objectives
- Assess the relationships between brain glucose metabolism, electrical activity as well as structural and functional connectivity during resting state in healthy subjects, including the establishment of a reference dataset of the above parameters.
- Synthesize a [18F]FDG-PET healthy Digital Twin from MRI.
- To investigate the relationship between neuropsychological test performance and the connectivity within and between different resting-state networks as well as glucose metabolism in relevant brain regions.
- Develop an individualized whole-brain model to capture functional hierarchy and investigate its change across aging.
Secondary Objectives
- To identify potential resting state fMRI and glucose metabolism biomarkers that predict cognitive performance and may serve as early indicators of cognitive decline.
- Analyze correlations between model-based and model-free measurements of brain hierarchy and behavioral outcomes, including neuropsychological tests.
Involved persons: Gitte Moos Knudsen and Charlotte Havelund Nykjær.
References
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