There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Marvin Petersen |
| Institution: | University Medical Center Hamburg-Eppendorf |
| Department: | Department of Neurology |
| Country: | |
| Proposed Analysis: | Micro- and Macrostructural Network Characteristics of Age-related Vascular Cognitive Impairment Research questions and aims This project aims to investigate the predictive capacity of innovative brain imaging markers of brain network characteristics regarding vascular cognitive impairment (VCI). We will focus on novel measures of functional and structural large-scale-brain network topology and dynamics. Predictive value of imaging markers will be adjusted for known risk factors of VCI. Our aim is to identify early risk markers in brain imaging supporting timely identification of individuals at risk for VCI and to guide future studies of early preventive measures. We plan to focus our investigation on cerebral small vessel disease (CSVD) as the most common cause of vascular cognitive impairment. Questions to be addressed are: * How does CSVD in early and progressive stages alter cerebral white matter microstructure, cortical atrophy and topology of large-scale brain networks? * Does cortical atrophy in CSVD preferentially occur in network hubs, brain regions of strongest connectivity? * Which brain regions pose disease epicenters, i.e., regions where atrophy and connectivity alteration coincide? * What is the spreading pattern of vascular pathology in the human connectome? * How do imaging markers of brain network characteristics contribute to predicting vascular cognitive impairment adjusted for demographic factors? Scientific rationale Vascular cognitive impairment (VCI) describes a continuum of clinical phenotypes ranging from mild cognitive impairment to manifest dementia. It is a disabling condition and poses a major challenge for industrialised societies. Causes of VCI are manifold. In terms of pathophysiological changes, CSVD is the most common cause, risks for conversion to vascular dementia are increased in this condition. In brain imaging, CSVD manifests in various features comprising (among others) white matter hyperintensities (WMH) and brain atrophy. Even in early stages of CSVD, subtle structural injury in the 'normal appearing white matter' can be detected by MR imaging markers. Etiological mechanisms between CSVD and cognitive impairment remain poorly understood. Previous studies indicate that disturbances of large-scale brain networks underlying cognitive functions play a key role. Recent work from our group and others suggested that reduced cognitive performance and CSVD are linked by the brain's impaired capabilities of relaying information between remote brain regions. WMH appear to preferentially disrupt long-range structural connectivity. Furthermore, cortical atrophy seems to preferentially occur in brain areas connected to underlying white matter injury. Alterations in brain regions central for network function, so called 'hubs' and the propagation of CSVD-related alterations in large-scale brain networks are promising novel candidates for determining VCI that have received less attention. This project aims to elucidate the impact of CSVD on large-scale brain networks mediating VCI. We will provide an in-depth analysis on the disease development and the relationship between CSVD, altered connectome characteristics and cognitive sequelae. We will investigate how imaging markers of CSVD contribute to prediction of VCI in addition to known epidemiological risk factors. Methods The key scientific interest of or research group is the understanding of network effects induced by structural brain injury and how this relates to clinical impairment in cerebrovascular diseases (www.csi-lab.de). In this project, we plan to investigate VCI in multiple cohorts: a population-based cohort at risk for cerebrovascular disease (Hamburg City Health Study) and a clinically-defined cohort with cognitive impairment exhibiting no amyloid aggregation in PET imaging (ADNI). Quantification of CSVD disease burden will take place via automated segmentation of WMH in T2-FLAIR weighted MR images (BIANCA). We will reconstruct large-scale brain networks, the 'connectome', based on structural, diffusion weighted and resting-state functional MRI. Network topology will be described using methods from graph theory. We plan to characterise subtle, fiber-specific alteration of white matter integrity using fixel-based analysis, a novel framework allowing assessment of white matter tract-specific microstructural properties based on diffusion imaging data. Cortical atrophy will be operationalized by analysis of cortical thickness. Investigating novel connectome markers, we will perform nodal stress modelling and disease epicentre mapping. According to the nodal stress hypothesis, particularly well-connected network parts, so called hubs, are more susceptible to pathology. We aim to test whether this hypothesis does also apply to CSVD by identifying hubs and investigating whether their atrophy severity correlates with their connectivity. Neurodegenerative pathologies causing cognitive decline is considered to start in distinct disease epicentres and to propagate over the brain network. However, whether a specific and systematic pattern of spreading pathology can be found in CSVD and VCI remains an open question. We will address this by identifying disease epicentres based on structural brain imaging data. Prediction of VCI will be based on test scores from cognitive testing in multiple domains leveraging features from brain imaging biomarkers of vascular risk. We will apply supervised machine-learning algorithms and conventional linear statistics for risk prediction models. The type and size of dataset required Access to the full anonymized brain imaging (DICOM where possible) and clinical data of the ADNI cohort is required. Expected value of the research Dementia and cognitive impairment are key causes of disability and dependency in ageing industrialised societies and a severe public health issue. Vascular dementia is considered the second most common cause of dementia following Alzheimer's disease. Our approach will elucidate the mechanisms by which altered brain structure in CSVD leads to VCI. Our predictive risk models will provide novel imaging markers of vascular brain injury associated with VCI. By this, we will contribute to early individualized risk prediction for VCI and facilitate timely preventive intervention. |
| Additional Investigators | |
| Investigator's Name: | Bastian Cheng |
| Proposed Analysis: | The investigator will be involved in the same analysis as described beforehand. |
| Investigator's Name: | Götz Thomalla |
| Proposed Analysis: | The investigator will be involved in the same analysis as described beforehand. |
| Investigator's Name: | Eckhard Schlemm |
| Proposed Analysis: | The investigator will be involved in the same analysis as described beforehand. |
| Investigator's Name: | Carola Mayer |
| Proposed Analysis: | The investigator will be involved in the same analysis as described beforehand. |
| Investigator's Name: | Felix Nägele |
| Proposed Analysis: | The investigator will be involved in the same analysis as described beforehand. |
