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Introduction Loss of blood-brain barrier (BBB) integrity is hypothesised to be one of the earliest microvascular signs of Alzheimer’s disease (AD). Existing BBB integrity imaging methods involve contrast agents or ionising radiation, and pose limitations in terms of cost and logistics. Arterial spin labelling (ASL) perfusion MRI has been recently adapted to map the BBB permeability non-invasively. The DEveloping BBB-ASL as a non-Invasive Early biomarker (DEBBIE) consortium aims to develop this modified ASL-MRI technique for patient-specific and robust BBB permeability assessments. This article outlines the study design of the DEBBIE cohorts focused on investigating the potential of BBB-ASL as an early biomarker for AD (DEBBIE-AD). Methods and analysis DEBBIE-AD consists of a multicohort study enrolling participants with subjective cognitive decline, mild cognitive impairment and AD, as well as age-matched healthy controls, from 13 cohorts. The precision and accuracy of BBB-ASL will be evaluated in healthy participants. The clinical value of BBB-ASL will be evaluated by comparing results with both established and novel AD biomarkers. The DEBBIE-AD study aims to provide evidence of the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD and ADrelated pathologies. Ethics and dissemination Ethics approval was obtained for 10 cohorts, and is pending for 3 cohorts. The results of the main trial and each of the secondary endpoints will be submitted for publication in a peer-reviewed journal.

One of the earliest signs of Alzheimer’s disease (AD) is the loss of blood-brain barrier (BBB) integrity. Arterial spin labeling (ASL) MRI is a non-invasive way to measure perfusion and several other hemodynamic and physiological parameters, including vascular permeability. The DEveloping BBB-ASL as non-Invasive Early biomarker (DEBBIE) consortium aims to develop and integrate innovative techniques to allow robust BBB permeability assessments by ASL to develop a sensitive, non-invasive, and early biomarker for AD and related dementias. This work summarizes our planned efforts to develop and establish an MRI-based BBB permeability biomarker.

Introduction: Loss of blood-brain barrier (BBB) integrity is hypothesized to be one of the earliest microvascular signs of Alzheimer’s disease (AD). Arterial spin labeling (ASL) perfusion MRI has recently been adapted to map the BBB permeability non-invasively. This article outlines the study design of the DEveloping BBB-ASL as a non-Invasive Early biomarker (DEBBIE) consortium, focused on investigating the potential of BBB-ASL as an early biomarker for AD (DEBBIE-AD). Methods: DEBBIE-AD consists of 13 cohorts enrolling participants from subjective cognitive decline to AD, as well as healthy controls across the lifespan. The reproducibility and accuracy of BBB-ASL will be evaluated in healthy participants, and its clinical value will be evaluated with both established and novel AD biomarkers. Expected endpoints: DEBBIE-AD aims to provide evidence on the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD-related pathologies, which may provide new targets for treatment.

One of the earliest signs of Alzheimer’s disease (AD) is the loss of blood-brain barrier (BBB) integrity. Arterial spin labeling (ASL) MRI is a non-invasive way to measure perfusion and several other hemodynamic and physiological parameters, including vascular permeability. The DEveloping BBB-ASL as non-Invasive Early biomarker (DEBBIE) consortium aims to develop and integrate innovative techniques to allow robust BBB permeability assessments by ASL to develop a sensitive, non-invasive, and early biomarker for AD and related dementias. This work summarizes our planned efforts to develop and establish an MRI-based BBB permeability biomarker.

Background Cerebrovascular disease (CVD) can increase the risk of dementia and is frequently seen in Alzheimer’s disease. Tissue inhibitor of proteinase-1 (TIMP-1) measured in cerebrospinal fluid (CBF) is considered a promising biomarker of subcortical small vessel disease (SSVD). In addition to inhibition of matrix metalloproteases, it occurs in several biological processes, such as protection of the blood brain barrier. This study explores how TIMP-1 is associated with CBF in amyloid and non-amyloid pre-dementia cases as well as in controls. Methods Cases and controls, aged 40-80, were included from the Norwegian multi-site study “DDI” (n=69, age=63.2 +/- SD, m/f=26/43). Participants underwent cognitive assessment, MRI and lumbar puncture. Cases were staged as Subjective Cognitive Decline (SCD) or Mild Cognitive Impairment (MCI). We stratified by A1-42 pathology (A+/-) using an amyloid-PET verified CSF cutoff. Cerebral blood flow (CBF) was measured with arterial spin labeling and analyzed using ExploreASL. Linear regression analysis was performed with TIMP-1 and CBF in total gray matter (GM), total white matter (WM) and several GM regions (frontal, temporal, insula, parietal, occipital, thalamus, putamen and caudate nucleus), adjusting for age and sex. CBF was log-transformed. Results In the A- group (n=50, age=61.6, m/f=17/33, Controls/SCD/MCI=21/22/7) there was a significant positive relationship between TIMP-1 and CBF in total GM (=-0.46, p=0.001), total WM (=-0.44, p=0.002), frontal (=-0.41, p=0.004), temporal (=-0.45, p=0.001), insula (=-0.39, p=0.006), parietal (=-0.48, p<0.001), occipital (=-0.45, p=0.001), thalamus (=-0.53, p<0.001), putamen (=-0.41, p=0.004) and caudate nucleus (=-0.48, p=0.001) GM regions. There were no significant associations in the A+ group (n=19, age=67.4, m/f=9/10, Controls/SCD/MCI=2/7/10) or in the whole sample. Conclusion We found that TIMP-1 correlates positively with CBF in the A- group, whereas there were no sign

Background Cerebrovascular disease (CVD) can increase the risk of dementia and is frequently seen in Alzheimer’s disease. Tissue inhibitor of proteinase-1 (TIMP-1) measured in cerebrospinal fluid (CBF) is considered a promising biomarker of subcortical small vessel disease (SSVD). In addition to inhibition of matrix metalloproteases, it occurs in several biological processes, such as protection of the blood brain barrier. This study explores how TIMP-1 is associated with CBF in amyloid and non-amyloid pre-dementia cases as well as in controls. Methods Cases and controls, aged 40-80, were included from the Norwegian multi-site study “DDI” (n=69, age=63.2 +/- SD, m/f=26/43). Participants underwent cognitive assessment, MRI and lumbar puncture. Cases were staged as Subjective Cognitive Decline (SCD) or Mild Cognitive Impairment (MCI). We stratified by A1-42 pathology (A+/-) using an amyloid-PET verified CSF cutoff. Cerebral blood flow (CBF) was measured with arterial spin labeling and analyzed using ExploreASL. Linear regression analysis was performed with TIMP-1 and CBF in total gray matter (GM), total white matter (WM) and several GM regions (frontal, temporal, insula, parietal, occipital, thalamus, putamen and caudate nucleus), adjusting for age and sex. CBF was log-transformed. Results In the A- group (n=50, age=61.6, m/f=17/33, Controls/SCD/MCI=21/22/7) there was a significant positive relationship between TIMP-1 and CBF in total GM (=-0.46, p=0.001), total WM (=-0.44, p=0.002), frontal (=-0.41, p=0.004), temporal (=-0.45, p=0.001), insula (=-0.39, p=0.006), parietal (=-0.48, p<0.001), occipital (=-0.45, p=0.001), thalamus (=-0.53, p<0.001), putamen (=-0.41, p=0.004) and caudate nucleus (=-0.48, p=0.001) GM regions. There were no significant associations in the A+ group (n=19, age=67.4, m/f=9/10, Controls/SCD/MCI=2/7/10) or in the whole sample. Conclusion We found that TIMP-1 correlates positively with CBF in the A- group, whereas there were no sign

Decreased/Poor vascular health - e.g. increased vascular resistance, tortuosity - may lead to a delayed arrival of labelled blood to brain tissue causing vascular artefacts on arterial spin labeling (ASL) images [Figure 1]. Although these artefacts are traditionally treated as a nuisance, their presence can be quantified by the spatial coefficient of variance (CoV) parameter and used as an indication of vascular insufficiency. . Here, the goal was to investigate the ability of spatial CoV to assess the vascular health by comparing it between healthy controls and subjects with Alzheimer’s (AD) and Parkinson’s (PD) diseases. Methods: We analyzed the MRI scans of 143 APGeM study participants, including healthy controls (HC, n=56) and subjects with AD- (n=41) or PD-related (n=46) mild cognitive impairment or dementia [Table]. We calculated CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlations of spatial CoV with age and WMH volume were investigated, as well as a t-test for the relation between spatial CoV and sex. A linear regression model was used to evaluate whether spatial CoV was able to discriminate HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. Results: Spatial CoV measures showed a positive correlation with age (cor=0.35, p <0.001 and with WMH volume (cor=0.38, p <0.001 [Figure 2A], and differed between sexes (p <0.001). Differences in spatial CoV values were detected between HC and AD and between HC and PD subjects (p<0.05 in both cases) [Figure 2B]. With our model, spatial CoV was not able to discriminate between AD and PD. Conclusions: These findings suggest that spatial CoV can provide insight in the vascular component of AD and PD pathologies. Validation of these results in larger cohorts and across a wider range of disorders might provide further insight in the relation between cerebrovascular health and neurodegeneration.

Background: In elderly populations, cerebral blood perfusion (CBF) measurements with arterial spin labelling (ASL) are affected by macro-vascular artefacts as a result of prolonged arterial transit time (ATT) [Figure 1]. The spatial coefficient of variance (CoV) provides a robust indirect estimate of ATT, hence it might be used as a proxy of the vascular health of the subject2, alone or in conjunction with other measures such as white matter hyperintensities (WMH) volume. The goal of this study was to compare spatial CoV between healthy controls (HCs) and subjects with Alzheimer’s (AD) or Parkinson’s (PD) disease. Methods: We analysed the scans of 143 participants from the APGeM study, in which a 3D GRASE pulsed ASL protocol was added to the Siemens 3T MRI protocol for degenerative diseases. Healthy controls (HC, n=56) and participants with AD- or PD-related mild cognitive impairment or dementia (AD, n=41; PD, n=46) were included [Table 1]. We calculated grey matter (GM) CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlation was used to calculate the correlation of spatial CoV with age and WMH volume. Student’s-test was performed to check differences in spatial CoV between sexes. A linear regression model was built to investigate whether spatial CoV could discriminate between HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. WMH and spatial CoV were logtransformed. Results: Spatial CoV showed a positive correlation with age (cor=0.35, p<0.001) and with WMH volume (cor=0.38, p <0.001) [Figure 2]. A difference in spatial CoV of 0.12 was reported (p<0.001). Spatial CoV differences were detected between HC and AD (ß=0.08, p=0.004) and between HC and PD (ß=0.09, p=0.0002) subjects [Figure 3]. However, spatial CoV was not able to discriminate between AD and PD. Conclusions: The relatively strong correlation between spatial CoV and WMH volume make ASL a promising marker to investigate the interplay between large and small vessel

Background: In elderly populations, cerebral blood perfusion (CBF) measurements with arterial spin labelling (ASL) are affected by macro-vascular artefacts as a result of prolonged arterial transit time (ATT) [Figure 1]. The spatial coefficient of variance (CoV) provides a robust indirect estimate of ATT, hence it might be used as a proxy of the vascular health of the subject2, alone or in conjunction with other measures such as white matter hyperintensities (WMH) volume. The goal of this study was to compare spatial CoV between healthy controls (HCs) and subjects with Alzheimer’s (AD) or Parkinson’s (PD) disease. Methods: We analysed the scans of 143 participants from the APGeM study, in which a 3D GRASE pulsed ASL protocol was added to the Siemens 3T MRI protocol for degenerative diseases. Healthy controls (HC, n=56) and participants with AD- or PD-related mild cognitive impairment or dementia (AD, n=41; PD, n=46) were included [Table 1]. We calculated grey matter (GM) CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlation was used to calculate the correlation of spatial CoV with age and WMH volume. Student’s-test was performed to check differences in spatial CoV between sexes. A linear regression model was built to investigate whether spatial CoV could discriminate between HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. WMH and spatial CoV were logtransformed. Results: Spatial CoV showed a positive correlation with age (cor=0.35, p<0.001) and with WMH volume (cor=0.38, p <0.001) [Figure 2]. A difference in spatial CoV of 0.12 was reported (p<0.001). Spatial CoV differences were detected between HC and AD (ß=0.08, p=0.004) and between HC and PD (ß=0.09, p=0.0002) subjects [Figure 3]. However, spatial CoV was not able to discriminate between AD and PD. Conclusions: The relatively strong correlation between spatial CoV and WMH volume make ASL a promising marker to investigate the interplay between large and small vessel

Decreased/Poor vascular health - e.g. increased vascular resistance, tortuosity - may lead to a delayed arrival of labelled blood to brain tissue causing vascular artefacts on arterial spin labeling (ASL) images [Figure 1]. Although these artefacts are traditionally treated as a nuisance, their presence can be quantified by the spatial coefficient of variance (CoV) parameter and used as an indication of vascular insufficiency. . Here, the goal was to investigate the ability of spatial CoV to assess the vascular health by comparing it between healthy controls and subjects with Alzheimer’s (AD) and Parkinson’s (PD) diseases. Methods: We analyzed the MRI scans of 143 APGeM study participants, including healthy controls (HC, n=56) and subjects with AD- (n=41) or PD-related (n=46) mild cognitive impairment or dementia [Table]. We calculated CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlations of spatial CoV with age and WMH volume were investigated, as well as a t-test for the relation between spatial CoV and sex. A linear regression model was used to evaluate whether spatial CoV was able to discriminate HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. Results: Spatial CoV measures showed a positive correlation with age (cor=0.35, p <0.001 and with WMH volume (cor=0.38, p <0.001 [Figure 2A], and differed between sexes (p <0.001). Differences in spatial CoV values were detected between HC and AD and between HC and PD subjects (p<0.05 in both cases) [Figure 2B]. With our model, spatial CoV was not able to discriminate between AD and PD. Conclusions: These findings suggest that spatial CoV can provide insight in the vascular component of AD and PD pathologies. Validation of these results in larger cohorts and across a wider range of disorders might provide further insight in the relation between cerebrovascular health and neurodegeneration.

Background: In elderly populations, cerebral blood perfusion (CBF) measurements with arterial spin labelling (ASL) are affected by macro-vascular artefacts as a result of prolonged arterial transit time (ATT) [Figure 1]. The spatial coefficient of variance (CoV) provides a robust indirect estimate of ATT, hence it might be used as a proxy of the vascular health of the subject2, alone or in conjunction with other measures such as white matter hyperintensities (WMH) volume. The goal of this study was to compare spatial CoV between healthy controls (HCs) and subjects with Alzheimer’s (AD) or Parkinson’s (PD) disease. Methods: We analysed the scans of 143 participants from the APGeM study, in which a 3D GRASE pulsed ASL protocol was added to the Siemens 3T MRI protocol for degenerative diseases. Healthy controls (HC, n=56) and participants with AD- or PD-related mild cognitive impairment or dementia (AD, n=41; PD, n=46) were included [Table 1]. We calculated grey matter (GM) CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlation was used to calculate the correlation of spatial CoV with age and WMH volume. Student’s-test was performed to check differences in spatial CoV between sexes. A linear regression model was built to investigate whether spatial CoV could discriminate between HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. WMH and spatial CoV were logtransformed. Results: Spatial CoV showed a positive correlation with age (cor=0.35, p<0.001) and with WMH volume (cor=0.38, p <0.001) [Figure 2]. A difference in spatial CoV of 0.12 was reported (p<0.001). Spatial CoV differences were detected between HC and AD (ß=0.08, p=0.004) and between HC and PD (ß=0.09, p=0.0002) subjects [Figure 3]. However, spatial CoV was not able to discriminate between AD and PD. Conclusions: The relatively strong correlation between spatial CoV and WMH volume make ASL a promising marker to investigate the interplay between large and small vessel

Background Cerebrovascular disease (CVD) can increase the risk of dementia and is frequently seen in Alzheimer’s disease. Tissue inhibitor of proteinase-1 (TIMP-1) measured in cerebrospinal fluid (CBF) is considered a promising biomarker of subcortical small vessel disease (SSVD). In addition to inhibition of matrix metalloproteases, it occurs in several biological processes, such as protection of the blood brain barrier. This study explores how TIMP-1 is associated with CBF in amyloid and non-amyloid pre-dementia cases as well as in controls. Methods Cases and controls, aged 40-80, were included from the Norwegian multi-site study “DDI” (n=69, age=63.2 +/- SD, m/f=26/43). Participants underwent cognitive assessment, MRI and lumbar puncture. Cases were staged as Subjective Cognitive Decline (SCD) or Mild Cognitive Impairment (MCI). We stratified by A1-42 pathology (A+/-) using an amyloid-PET verified CSF cutoff. Cerebral blood flow (CBF) was measured with arterial spin labeling and analyzed using ExploreASL. Linear regression analysis was performed with TIMP-1 and CBF in total gray matter (GM), total white matter (WM) and several GM regions (frontal, temporal, insula, parietal, occipital, thalamus, putamen and caudate nucleus), adjusting for age and sex. CBF was log-transformed. Results In the A- group (n=50, age=61.6, m/f=17/33, Controls/SCD/MCI=21/22/7) there was a significant positive relationship between TIMP-1 and CBF in total GM (=-0.46, p=0.001), total WM (=-0.44, p=0.002), frontal (=-0.41, p=0.004), temporal (=-0.45, p=0.001), insula (=-0.39, p=0.006), parietal (=-0.48, p<0.001), occipital (=-0.45, p=0.001), thalamus (=-0.53, p<0.001), putamen (=-0.41, p=0.004) and caudate nucleus (=-0.48, p=0.001) GM regions. There were no significant associations in the A+ group (n=19, age=67.4, m/f=9/10, Controls/SCD/MCI=2/7/10) or in the whole sample. Conclusion We found that TIMP-1 correlates positively with CBF in the A- group, whereas there were no sign

Decreased/Poor vascular health - e.g. increased vascular resistance, tortuosity - may lead to a delayed arrival of labelled blood to brain tissue causing vascular artefacts on arterial spin labeling (ASL) images [Figure 1]. Although these artefacts are traditionally treated as a nuisance, their presence can be quantified by the spatial coefficient of variance (CoV) parameter and used as an indication of vascular insufficiency. . Here, the goal was to investigate the ability of spatial CoV to assess the vascular health by comparing it between healthy controls and subjects with Alzheimer’s (AD) and Parkinson’s (PD) diseases. Methods: We analyzed the MRI scans of 143 APGeM study participants, including healthy controls (HC, n=56) and subjects with AD- (n=41) or PD-related (n=46) mild cognitive impairment or dementia [Table]. We calculated CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlations of spatial CoV with age and WMH volume were investigated, as well as a t-test for the relation between spatial CoV and sex. A linear regression model was used to evaluate whether spatial CoV was able to discriminate HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. Results: Spatial CoV measures showed a positive correlation with age (cor=0.35, p <0.001 and with WMH volume (cor=0.38, p <0.001 [Figure 2A], and differed between sexes (p <0.001). Differences in spatial CoV values were detected between HC and AD and between HC and PD subjects (p<0.05 in both cases) [Figure 2B]. With our model, spatial CoV was not able to discriminate between AD and PD. Conclusions: These findings suggest that spatial CoV can provide insight in the vascular component of AD and PD pathologies. Validation of these results in larger cohorts and across a wider range of disorders might provide further insight in the relation between cerebrovascular health and neurodegeneration.

Background: In elderly populations, cerebral blood perfusion (CBF) measurements with arterial spin labelling (ASL) are affected by macro-vascular artefacts as a result of prolonged arterial transit time (ATT) [Figure 1]. The spatial coefficient of variance (CoV) provides a robust indirect estimate of ATT, hence it might be used as a proxy of the vascular health of the subject2, alone or in conjunction with other measures such as white matter hyperintensities (WMH) volume. The goal of this study was to compare spatial CoV between healthy controls (HCs) and subjects with Alzheimer’s (AD) or Parkinson’s (PD) disease. Methods: We analysed the scans of 143 participants from the APGeM study, in which a 3D GRASE pulsed ASL protocol was added to the Siemens 3T MRI protocol for degenerative diseases. Healthy controls (HC, n=56) and participants with AD- or PD-related mild cognitive impairment or dementia (AD, n=41; PD, n=46) were included [Table 1]. We calculated grey matter (GM) CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlation was used to calculate the correlation of spatial CoV with age and WMH volume. Student’s-test was performed to check differences in spatial CoV between sexes. A linear regression model was built to investigate whether spatial CoV could discriminate between HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. WMH and spatial CoV were logtransformed. Results: Spatial CoV showed a positive correlation with age (cor=0.35, p<0.001) and with WMH volume (cor=0.38, p <0.001) [Figure 2]. A difference in spatial CoV of 0.12 was reported (p<0.001). Spatial CoV differences were detected between HC and AD (ß=0.08, p=0.004) and between HC and PD (ß=0.09, p=0.0002) subjects [Figure 3]. However, spatial CoV was not able to discriminate between AD and PD. Conclusions: The relatively strong correlation between spatial CoV and WMH volume make ASL a promising marker to investigate the interplay between large and small vessel

Background Cerebrovascular disease (CVD) can increase the risk of dementia and is frequently seen in Alzheimer’s disease. Tissue inhibitor of proteinase-1 (TIMP-1) measured in cerebrospinal fluid (CBF) is considered a promising biomarker of subcortical small vessel disease (SSVD). In addition to inhibition of matrix metalloproteases, it occurs in several biological processes, such as protection of the blood brain barrier. This study explores how TIMP-1 is associated with CBF in amyloid and non-amyloid pre-dementia cases as well as in controls. Methods Cases and controls, aged 40-80, were included from the Norwegian multi-site study “DDI” (n=69, age=63.2 +/- SD, m/f=26/43). Participants underwent cognitive assessment, MRI and lumbar puncture. Cases were staged as Subjective Cognitive Decline (SCD) or Mild Cognitive Impairment (MCI). We stratified by A1-42 pathology (A+/-) using an amyloid-PET verified CSF cutoff. Cerebral blood flow (CBF) was measured with arterial spin labeling and analyzed using ExploreASL. Linear regression analysis was performed with TIMP-1 and CBF in total gray matter (GM), total white matter (WM) and several GM regions (frontal, temporal, insula, parietal, occipital, thalamus, putamen and caudate nucleus), adjusting for age and sex. CBF was log-transformed. Results In the A- group (n=50, age=61.6, m/f=17/33, Controls/SCD/MCI=21/22/7) there was a significant positive relationship between TIMP-1 and CBF in total GM (=-0.46, p=0.001), total WM (=-0.44, p=0.002), frontal (=-0.41, p=0.004), temporal (=-0.45, p=0.001), insula (=-0.39, p=0.006), parietal (=-0.48, p<0.001), occipital (=-0.45, p=0.001), thalamus (=-0.53, p<0.001), putamen (=-0.41, p=0.004) and caudate nucleus (=-0.48, p=0.001) GM regions. There were no significant associations in the A+ group (n=19, age=67.4, m/f=9/10, Controls/SCD/MCI=2/7/10) or in the whole sample. Conclusion We found that TIMP-1 correlates positively with CBF in the A- group, whereas there were no sign

Decreased/Poor vascular health - e.g. increased vascular resistance, tortuosity - may lead to a delayed arrival of labelled blood to brain tissue causing vascular artefacts on arterial spin labeling (ASL) images [Figure 1]. Although these artefacts are traditionally treated as a nuisance, their presence can be quantified by the spatial coefficient of variance (CoV) parameter and used as an indication of vascular insufficiency. . Here, the goal was to investigate the ability of spatial CoV to assess the vascular health by comparing it between healthy controls and subjects with Alzheimer’s (AD) and Parkinson’s (PD) diseases. Methods: We analyzed the MRI scans of 143 APGeM study participants, including healthy controls (HC, n=56) and subjects with AD- (n=41) or PD-related (n=46) mild cognitive impairment or dementia [Table]. We calculated CBF, spatial CoV, and WMH volume using ExploreASL [ref]. Pearson’s correlations of spatial CoV with age and WMH volume were investigated, as well as a t-test for the relation between spatial CoV and sex. A linear regression model was used to evaluate whether spatial CoV was able to discriminate HC vs. AD, HC vs. PD, and AD vs. PD after correction for age and sex. Results: Spatial CoV measures showed a positive correlation with age (cor=0.35, p <0.001 and with WMH volume (cor=0.38, p <0.001 [Figure 2A], and differed between sexes (p <0.001). Differences in spatial CoV values were detected between HC and AD and between HC and PD subjects (p<0.05 in both cases) [Figure 2B]. With our model, spatial CoV was not able to discriminate between AD and PD. Conclusions: These findings suggest that spatial CoV can provide insight in the vascular component of AD and PD pathologies. Validation of these results in larger cohorts and across a wider range of disorders might provide further insight in the relation between cerebrovascular health and neurodegeneration.