Cerebral perfusion in the predementia stages of Alzheimer’s disease

Binnewijzend, Maja; Benedictus, Marije R.; Kuijer, Joost P.A.; Flier, Wiesje M. van der; Teunissen, Charlotte E.; Prins, Niels D.; Wattjes, Mike P.; Berckel, Bart N.M. van; Scheltens, Philip; Barkhof, Frederik

doi:10.1007/s00330-015-3834-9

Cited by 111 publications

(115 citation statements)

References 34 publications

(46 reference statements)

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“…In concordance with previous ASL studies [7][8][9][10][11]16], highest accuracies for SVM training were observed using AD-specific ROIs instead of whole-brain. For AD vs. SCD, the combined parietal lobe and hippocampus ROI yielded the highest accuracy.…”

Section: Discussionsupporting

confidence: 90%

“…Although the use of the parietal lobe is in line with previous ASL results [10,11], application of the occipital lobe is contradictive, as occipital hypo-perfusion has been observed in patients with MCI compared to controls, but not compared to patients with AD [11,13]. The fact that the majority of our AD sample consisted of early-onset AD patients, who present with a more pronounced overall and occipital hypo-perfusion and hypo-metabolism compared to late-onset AD patients [33,34], could explain this discrepancy.…”

Section: Discussionsupporting

confidence: 86%

“…Subsequently, masks of Alzheimer-specific regions of interest (ROI) were used to maximize training accuracies. ROIs were based on the literature [1,7,8,10,11,16] and thresholded group mean perfusion maps. They included the parietal lobe, hippocampus, occipital lobe, and combinations thereof.…”

Section: Svm: Multivariate Pattern Recognition In Training-setmentioning

confidence: 99%

“…Whole-brain hypo-perfusion as measured by dedicated MR scans has been demonstrated in patients with AD compared to controls [7,8], most prominently in the parietal lobe structures such as the posterior cingulate cortex (PCC), precuneus (PC), and inferior parietal lobule (IPL) [7][8][9]. Patients with mild cognitive impairment (MCI) show similar but less pronounced hypo-perfusion patterns compared to patients with AD [10], with quantitative cerebral blood flow (CBF) values intermediate between those of patients with AD and controls [11]. Hypo-perfusion has also been reported in the occipital and temporal lobes in patients with MCI compared to controls [11].…”

Section: Introductionmentioning

confidence: 99%

“…Patients with mild cognitive impairment (MCI) show similar but less pronounced hypo-perfusion patterns compared to patients with AD [10], with quantitative cerebral blood flow (CBF) values intermediate between those of patients with AD and controls [11]. Hypo-perfusion has also been reported in the occipital and temporal lobes in patients with MCI compared to controls [11].…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Collij¹,

Heeman²,

Kuijer³

et al. 2016

Radiology

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This manuscript is an original research paperWord count: 3015 Advances in Knowledge:-Automated machine learning methods can be trained to distinguish between subjects with subjective cognitive decline (SCD), patients with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) based on arterial spin labeling (ASL) images with high classification training accuracy (range, 83.8% -89.0% , p < .01).-Classifiers based on these trainings can predict the diagnosis of single subjects with high diagnostic accuracy (area under the receiver operating curve range, .89 -.96, p < .001). Implications for Patient Care:-Automated classification of 3D pseudo-continuous ASL scans that detects AD patients with high accuracy (> 82%) may support image-based diagnosis, especially in centres without experienced (neuro)radiologists.-Automated classification of 3D pseudo-continuous ASL scans may be used for AD screening purposes without compromising diagnostic accuracy.Summary statement: Automated classification of perfusion maps enable distinguishing patients with various stages of Alzheimer's disease with high accuracy.ABSTRACT Purpose: This current study investigates whether multivariate pattern recognition analysis of arterial spin labeling (ASL) perfusion maps can be used for classification and single-subject prediction of patients withAlzheimer's disease (AD), mild cognitive impairment (MCI), and subjective cognitive decline (SCD), after using the W-score method to remove confounding effects of gender and age. Materials and Methods:The local institutional review board approved the study. Subjects classifier used diagnostic status and perfusion maps. Discrimination maps were extracted and used for single-subject classification in the prediction-set. Prediction performance was assessed by means of a ROC analysis, generating an area under the curve (AUC) and sensitivity/specificity distribution.Results: Single-subject diagnosis in the prediction-set using the discrimination maps yielded excellent performance for AD vs. SCD (AUC .96, p < .01), good performance for AD vs. MCI (AUC = 0.89, p < .01), and poor performance for MCI vs. SCD (AUC = 0.63, p = .06).Application of the AD vs. SCD discrimination map for prediction of MCI subgroups resulted in good performance for MCIc vs. SCD (AUC = .84, p < .01) and fair performance for MCIc vs.MCIs (AUC = .71, p > .05). Conclusion:Using automated methods, age-and gender adjusted ASL perfusion maps can be used to classify and predict diagnoses of AD, MCI-converters, stable MCI patients and SCD subjects with good to excellent accuracy and AUC values.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 86%

Section: Svm: Multivariate Pattern Recognition In Training-setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Collij¹,

Heeman²,

Kuijer³

et al. 2016

Radiology

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Premenopausal bilateral oophorectomy and brain white matter brain integrity in later‐life

Mielke,

Frank,

Christenson

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONPremenopausal bilateral oophorectomy (PBO) is associated with later‐life cognition, but the underlying brain changes remain unclear. We assessed the impact of PBO and PBO age on white matter integrity.METHODSFemale participants with regional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA) and mean diffusivity (MD) were included (22 with PBO < 40 years; 43 with PBO 40‐45 years; 39 with PBO 46‐49 years; 907 referents without PBO < 50 years). Linear regression models adjusted for age and apolipoprotein E (APOE) genotype.RESULTSFemales with PBO < 40 years, compared to referents, had lower FA and higher MD in the anterior corona radiata, genu of the corpus collosum, inferior fronto‐occipital fasciculus, superior occipital, and superior temporal white matter. Females who underwent PBO between 45 and 49 also had some changes in white matter integrity.DISCUSSIONFemales who underwent PBO < 40 years had reduced white matter integrity across multiple regions in later‐life. These results are important for females considering PBO for noncancerous conditions.Highlights Females with premenopausal bilateral oophorectomy (PBO) < 40 years had lower FA versus referents. Females with PBO < 40 years had higher MD in many regions versus referents. Adjusting for estrogen replacement therapy use did not attenuate results. Females with PBO 45‐49 years also had some white matter changes versus referents.

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Preliminary Assessment of Intravoxel Incoherent Motion Diffusion‐Weighted MRI (IVIM‐DWI) Metrics in Alzheimer's Disease

Bergamino

Nespodzany

Baxter

et al. 2020

Magnetic Resonance Imaging

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Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that affects aging populations. Current MRI techniques are often limited in their sensitivity to underlying neuropathological changes. Purpose: To characterize differences in voxel-based morphometry (VBM), apparent diffusion coefficient (ADC), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) metrics in aging populations. Additionally, to investigate the connection between cognitive assessments and neuroimaging metrics. Study Type: Prospective/cross-sectional. Population: In all, 49 subjects, including 13 with AD dementia, 12 with mild cognitive impairment (MCI), and 24 healthy controls (HC). Field Strength/Sequence: 3T/magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and IVIM-DWI Assessment: All participants completed a cognitive screening battery prior to MRI. IVIM-DWI maps (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) were generated from a biexponential fit of diffusion MRI data. VBM was performed on the standard T 1-weighted MP-RAGE structural images. Group-wise templates were used to compare across groups. Statistical Tests: Analysis of covariance (ANCOVA) with gender and age as covariates (familywise error [FWE] corrected, post-hoc comparisons using Bonferroni correction) for group comparisons. Partial-η 2 and Hedges' g were used for effectsize analysis. Spearman's correlations (false discovery rate [FDR]-corrected) for the relationship between cognitive scores and imaging. Results: Clusters of significant group-wise differences were found mainly in the temporal lobe, hippocampus, and amygdala using all VBM and IVIM methods (P < 0.05 FWE). While VBM showed significant changes between MCI and AD groups and between HC and AD groups, no significant clusters were observed between HC and MCI using VBM. ADC and IVIM-D demonstrated significant changes, at P < 0.05 FWE, between HC and MCI, notably in the amygdala and hippocampus. Several voxel-based correlations were observed between neuroimaging metrics and cognitive tests within the cognitively impaired groups (P < 0.05 FDR). Data Conclusion: These findings suggest that IVIM-DWI metrics may be earlier biomarkers for AD-related changes than VBM. The use of these techniques may provide novel insight into subvoxel neurodegenerative processes. Level of Evidence: 2 Technical Efficacy Stage: 2

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Cerebral perfusion in the predementia stages of Alzheimer’s disease

Cited by 111 publications

References 34 publications

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Application of Machine Learning to Arterial Spin Labeling in Mild Cognitive Impairment and Alzheimer Disease

Premenopausal bilateral oophorectomy and brain white matter brain integrity in later‐life

Preliminary Assessment of Intravoxel Incoherent Motion Diffusion‐Weighted MRI (IVIM‐DWI) Metrics in Alzheimer's Disease

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