Project Details
Description
Background
Increasing evidence suggests that cerebrovascular health plays a major role in the progression of cognitive dysfunction. Current cerebrovascular health markers - e.g. white matter lesions - only represent the consequences of vascular damage. A direct and dynamic marker is needed to assess the deterioration of cerebrovascular health in an earlier stage and to closely monitor treatment effects. Arterial Spin Labeling (ASL) MRI provides such a dynamic measurement, and acts as a proxy for vascular and metabolic information of the brain. Recently, we developed an image processing technique that extracts the cerebrovascular information from ASL scans. Our preliminary studies showed that this novel parameter provides a proxy for global cerebrovascular health, as it does not only correlate with large- and small vessel disease parameters, but also differentiates healthy controls, and patients with Mild Cognitive Impairment and Alzheimers Disease.
Hypothesis
Our main hypothesis is that the cerebrovascular age captured from ASL images is a useful predictor of cognitive decline.
Aim of the study
We will develop ASL perfusion MRI as non-invasive biomarker of cerebrovascular age, by training the association between ASL images and chronological age in healthy volunteers and using this to predict the biological cerebrovascular age for secondary prevention and treatment monitoring in patients.
Research plan
CREATION - MR scans of cognitively unimpaired participants will be processed to extract regional mean CBF and heterogeneity information. The extracted values will be used to train and validate a probabilistic classifier to predict the cerebrovascular age fom a normal population. Further steps will be undertaken to generalize the classifier for ASL acquisitions from different MRI scanners, with different post-labeling delays and/or spatial resolutions. We will extrapolate the age-dependent regional CBF and sCoV changes from the NOMARED data by assuming that the sCoV and CBF in the earlier PLD's would correspond to later PLDs at a higher age. These data will be used to train an extended RVM classifier to be used in WPs 2 and 3, to detect biological and/or chronological cerebrovascular ages higher than the maximum age of the normal controls, and to boost the training in the normal age range.
INTERPRETATION - We will investigate the physiological meaning of the cerebrovascular age classifier by comparing the extent to which the cerebrovascular-chronological age gap is associated with standard imaging biomarkers of large (vessel volume on Magnetic Resonace Angiography (MRA)) and small vessel disease (white matter hyperintensities (WMH) volume). Subsequently, we will compare the cerebrovascular age classifier with the established BrainAGE structural age classifier, that is based on high resolution anatomical MRI images. This will put the cerebrovascular age biomarker in perspective of the existing established biomarkers that measure the consequences of vascular pathology.
APPLICATION - In a range of vascularly and cognitively compromised participants, the changes in the cerebrovascular-chronological age gap will be investigated. The complex relationship between the cardiovascular, cerebrovascular, and structural brain changes and their individual role in being a risk factor of cognitive impairment will be probed, with respect to different sex and ethnicity.
Increasing evidence suggests that cerebrovascular health plays a major role in the progression of cognitive dysfunction. Current cerebrovascular health markers - e.g. white matter lesions - only represent the consequences of vascular damage. A direct and dynamic marker is needed to assess the deterioration of cerebrovascular health in an earlier stage and to closely monitor treatment effects. Arterial Spin Labeling (ASL) MRI provides such a dynamic measurement, and acts as a proxy for vascular and metabolic information of the brain. Recently, we developed an image processing technique that extracts the cerebrovascular information from ASL scans. Our preliminary studies showed that this novel parameter provides a proxy for global cerebrovascular health, as it does not only correlate with large- and small vessel disease parameters, but also differentiates healthy controls, and patients with Mild Cognitive Impairment and Alzheimers Disease.
Hypothesis
Our main hypothesis is that the cerebrovascular age captured from ASL images is a useful predictor of cognitive decline.
Aim of the study
We will develop ASL perfusion MRI as non-invasive biomarker of cerebrovascular age, by training the association between ASL images and chronological age in healthy volunteers and using this to predict the biological cerebrovascular age for secondary prevention and treatment monitoring in patients.
Research plan
CREATION - MR scans of cognitively unimpaired participants will be processed to extract regional mean CBF and heterogeneity information. The extracted values will be used to train and validate a probabilistic classifier to predict the cerebrovascular age fom a normal population. Further steps will be undertaken to generalize the classifier for ASL acquisitions from different MRI scanners, with different post-labeling delays and/or spatial resolutions. We will extrapolate the age-dependent regional CBF and sCoV changes from the NOMARED data by assuming that the sCoV and CBF in the earlier PLD's would correspond to later PLDs at a higher age. These data will be used to train an extended RVM classifier to be used in WPs 2 and 3, to detect biological and/or chronological cerebrovascular ages higher than the maximum age of the normal controls, and to boost the training in the normal age range.
INTERPRETATION - We will investigate the physiological meaning of the cerebrovascular age classifier by comparing the extent to which the cerebrovascular-chronological age gap is associated with standard imaging biomarkers of large (vessel volume on Magnetic Resonace Angiography (MRA)) and small vessel disease (white matter hyperintensities (WMH) volume). Subsequently, we will compare the cerebrovascular age classifier with the established BrainAGE structural age classifier, that is based on high resolution anatomical MRI images. This will put the cerebrovascular age biomarker in perspective of the existing established biomarkers that measure the consequences of vascular pathology.
APPLICATION - In a range of vascularly and cognitively compromised participants, the changes in the cerebrovascular-chronological age gap will be investigated. The complex relationship between the cardiovascular, cerebrovascular, and structural brain changes and their individual role in being a risk factor of cognitive impairment will be probed, with respect to different sex and ethnicity.
| Acronym | CVAge |
|---|---|
| Status | Not started |