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: | Maria C Valdes Hernandez |
| Institution: | University of Edinburgh |
| Department: | Neuroimaging Sciences |
| Country: | |
| Proposed Analysis: | Title of the study: Automatic assessment of brain enlarged perivascular spaces from brain MRI to explore their role in brain resilience, cognition and cerebrovascular risk of dementia progression Lay Summary: Perivascular spaces (PVS) are very small fluid-filled spaces in the brain tissues that become increasingly visible on brain MRI in sleep-deprived individuals and patients who are developing vascular and neurodegenerative brain signs. They have recently received attention due to their role in clearing the waste from the brain metabolism, therefore being suspected to influence mental health, dementia onset and progression. Recent advances in MRI mean that high sensitivity 3D scans are more common and automated computational methods can now quantify PVS including their morphology. Thus, the ability to measure these structures on routine brain MRI offers a major opportunity for studying cognitive resilience and the early detection of persons at increased risk of dementia. The findings can translate rapidly to clinics to assess prognosis, aid diagnosis, monitor progression, accelerate development of therapies and monitor their response. The ADNI project is particularly important since it gathers longitudinal cognitive, biomarkers, and MRI data from a large well-characterised group of individuals . Hypotheses of the study: We hypothesise that PVS burden (count, volume) will be negatively associated with psychological health indicators, and a sensitive marker of cognitive decline and dementia progression. We expect to confirm the findings from a previous study who found evidence of important genetic contribution to PVS burden in older community-dwelling people (Duperron et al. 2018). Study Aims: 1. Demonstrate that computational measures of abnormal PVS are feasible in large scale studies and improve robustness to variation in scan quality and vendor. 2. Investigate if PVS location or morphology can be sensitive markers for cerebrovascular health indicators and cognitive reserve. 3. Test the PVS metrics against cognitive scores, vascular risk factors and variables from the clinical questionnaire. 4. Confirm the association between computationally-determined PVS morphologies and vascular dysfunctions, thus providing a defined biomarker in preparation for creating a clinically applicable prognostic and diagnostic tool. 5. Investigate the early life cognitive contribution to the PVS computational measurements. |
| Additional Investigators | |
| Investigator's Name: | Roberto Duarte Coello |
| Proposed Analysis: | Title of the study: Automatic assessment of brain enlarged perivascular spaces from brain MRI to explore their role in brain resilience, cognition and cerebrovascular risk of dementia progression Lay Summary: Perivascular spaces (PVS) are very small fluid-filled spaces in the brain tissues that become increasingly visible on brain MRI in sleep-deprived individuals and patients who are developing vascular and neurodegenerative brain signs. They have recently received attention due to their role in clearing the waste from the brain metabolism, therefore being suspected to influence mental health, dementia onset and progression. Recent advances in MRI mean that high sensitivity 3D scans are more common and automated computational methods can now quantify PVS including their morphology. Thus, the ability to measure these structures on routine brain MRI offers a major opportunity for studying cognitive resilience and the early detection of persons at increased risk of dementia. The findings can translate rapidly to clinics to assess prognosis, aid diagnosis, monitor progression, accelerate development of therapies and monitor their response. The ADNI project is particularly important since it gathers longitudinal health data from a large well-characterised group of individuals Hypotheses of the study: We hypothesise that PVS burden (count, volume) will be negatively associated with psychological health indicators, and a sensitive marker of cognitive decline and dementia progression. We expect to confirm the findings from a previous study who found evidence of important genetic contribution to PVS burden in older community-dwelling people (Duperron et al. 2018). Study Aims: 1. Demonstrate that computational measures of abnormal PVS are feasible in large scale studies and improve robustness to variation in scan quality and vendor. 2. Investigate if PVS location or morphology can be sensitive markers for cerebrovascular health indicators and cognitive reserve. 3. Test the PVS metrics against cognitive scores, vascular risk factors and variables from the clinical questionnaire. 4. Confirm the association between computationally-determined PVS morphologies and vascular dysfunctions, thus providing a defined biomarker in preparation for creating a clinically applicable prognostic and diagnostic tool. 5. Investigate the early life cognitive contribution to the PVS computational measurements. |
| Investigator's Name: | Lucia Ballerini |
| Proposed Analysis: | Title of the study: Automatic assessment of brain enlarged perivascular spaces from brain MRI to explore their role in brain resilience, cognition and cerebrovascular risk of dementia progression Lay Summary: Perivascular spaces (PVS) are very small fluid-filled spaces in the brain tissues that become increasingly visible on brain MRI in sleep-deprived individuals and patients who are developing vascular and neurodegenerative brain signs. They have recently received attention due to their role in clearing the waste from the brain metabolism, therefore being suspected to influence mental health, dementia onset and progression. Recent advances in MRI mean that high sensitivity 3D scans are more common and automated computational methods can now quantify PVS including their morphology. Thus, the ability to measure these structures on routine brain MRI offers a major opportunity for studying cognitive resilience and the early detection of persons at increased risk of dementia. The findings can translate rapidly to clinics to assess prognosis, aid diagnosis, monitor progression, accelerate development of therapies and monitor their response. The ADNI project is particularly important since it gathers data from a large well-characterised group of individuals (Habota et al. 2019). Hypotheses of the study: We hypothesise that PVS burden (count, volume) will be negatively associated with psychological health indicators, and a sensitive marker of cognitive decline and dementia progression. We expect to confirm the findings from a previous study who found evidence of important genetic contribution to PVS burden in older community-dwelling people (Duperron et al. 2018). Study Aims: 1. Demonstrate that computational measures of abnormal PVS are feasible in large scale studies and improve robustness to variation in scan quality and vendor. 2. Investigate if PVS location or morphology can be sensitive markers for cerebrovascular health indicators and cognitive reserve. 3. Test the PVS metrics against cognitive scores, vascular risk factors and variables from the clinical questionnaire. 4. Confirm the association between computationally-determined PVS morphologies and vascular dysfunctions, thus providing a defined biomarker in preparation for creating a clinically applicable prognostic and diagnostic tool. 5. Investigate the early life cognitive contribution to the PVS computational measurements. |
| Investigator's Name: | Joanna Wardlaw |
| Proposed Analysis: | Title of the study: Automatic assessment of brain enlarged perivascular spaces from brain MRI to explore their role in brain resilience, cognition and cerebrovascular risk of dementia progression Lay Summary: Perivascular spaces (PVS) are very small fluid-filled spaces in the brain tissues that become increasingly visible on brain MRI in sleep-deprived individuals and patients who are developing vascular and neurodegenerative brain signs. They have recently received attention due to their role in clearing the waste from the brain metabolism, therefore being suspected to influence mental health, dementia onset and progression. Recent advances in MRI mean that high sensitivity 3D scans are more common and automated computational methods can now quantify PVS including their morphology. Thus, the ability to measure these structures on routine brain MRI offers a major opportunity for studying cognitive resilience and the early detection of persons at increased risk of dementia. The findings can translate rapidly to clinics to assess prognosis, aid diagnosis, monitor progression, accelerate development of therapies and monitor their response. The ADNI project is particularly important since it gathers data from a large well-characterised group of individuals (Habota et al. 2019). Hypotheses of the study: We hypothesise that PVS burden (count, volume) will be negatively associated with psychological health indicators, and a sensitive marker of cognitive decline and dementia progression. We expect to confirm the findings from a previous study who found evidence of important genetic contribution to PVS burden in older community-dwelling people (Duperron et al. 2018). Study Aims: 1. Demonstrate that computational measures of abnormal PVS are feasible in large scale studies and improve robustness to variation in scan quality and vendor. 2. Investigate if PVS location or morphology can be sensitive markers for cerebrovascular health indicators and cognitive reserve. 3. Test the PVS metrics against cognitive scores, vascular risk factors and variables from the clinical questionnaire. 4. Confirm the association between computationally-determined PVS morphologies and vascular dysfunctions, thus providing a defined biomarker in preparation for creating a clinically applicable prognostic and diagnostic tool. 5. Investigate the early life cognitive contribution to the PVS computational measurements. |
| Investigator's Name: | Francesca Chappell |
| Proposed Analysis: | Title of the study: Automatic assessment of brain enlarged perivascular spaces from brain MRI to explore their role in brain resilience, cognition and cerebrovascular risk of dementia progression Lay Summary: Perivascular spaces (PVS) are very small fluid-filled spaces in the brain tissues that become increasingly visible on brain MRI in sleep-deprived individuals and patients who are developing vascular and neurodegenerative brain signs. They have recently received attention due to their role in clearing the waste from the brain metabolism, therefore being suspected to influence mental health, dementia onset and progression. Recent advances in MRI mean that high sensitivity 3D scans are more common and automated computational methods can now quantify PVS including their morphology. Thus, the ability to measure these structures on routine brain MRI offers a major opportunity for studying cognitive resilience and the early detection of persons at increased risk of dementia. The findings can translate rapidly to clinics to assess prognosis, aid diagnosis, monitor progression, accelerate development of therapies and monitor their response. The ADNI project is particularly important since it gathers data from a large well-characterised group of individuals (Habota et al. 2019). Hypotheses of the study: We hypothesise that PVS burden (count, volume) will be negatively associated with psychological health indicators, and a sensitive marker of cognitive decline and dementia progression. We expect to confirm the findings from a previous study who found evidence of important genetic contribution to PVS burden in older community-dwelling people (Duperron et al. 2018). Study Aims: 1. Demonstrate that computational measures of abnormal PVS are feasible in large scale studies and improve robustness to variation in scan quality and vendor. 2. Investigate if PVS location or morphology can be sensitive markers for cerebrovascular health indicators and cognitive reserve. 3. Test the PVS metrics against cognitive scores, vascular risk factors and variables from the clinical questionnaire. 4. Confirm the association between computationally-determined PVS morphologies and vascular dysfunctions, thus providing a defined biomarker in preparation for creating a clinically applicable prognostic and diagnostic tool. 5. Investigate the early life cognitive contribution to the PVS computational measurements. |
