| 1. |
- Jansen, Willemijn J, et al.
(författare)
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Association of Cerebral Amyloid-β Aggregation With Cognitive Functioning in Persons Without Dementia.
- 2018
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Ingår i: JAMA psychiatry. - : American Medical Association (AMA). - 2168-6238 .- 2168-622X. ; 75:1, s. 84-95
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Tidskriftsartikel (refereegranskat)abstract
- Cerebral amyloid-β aggregation is an early event in Alzheimer disease (AD). Understanding the association between amyloid aggregation and cognitive manifestation in persons without dementia is important for a better understanding of the course of AD and for the design of prevention trials.To investigate whether amyloid-β aggregation is associated with cognitive functioning in persons without dementia.This cross-sectional study included 2908 participants with normal cognition and 4133 with mild cognitive impairment (MCI) from 53 studies in the multicenter Amyloid Biomarker Study. Normal cognition was defined as having no cognitive concerns for which medical help was sought and scores within the normal range on cognitive tests. Mild cognitive impairment was diagnosed according to published criteria. Study inclusion began in 2013 and is ongoing. Data analysis was performed in January 2017.Global cognitive performance as assessed by the Mini-Mental State Examination (MMSE) and episodic memory performance as assessed by a verbal word learning test. Amyloid aggregation was measured with positron emission tomography or cerebrospinal fluid biomarkers and dichotomized as negative (normal) or positive (abnormal) according to study-specific cutoffs. Generalized estimating equations were used to examine the association between amyloid aggregation and low cognitive scores (MMSE score ≤27 or memory z score≤-1.28) and to assess whether this association was moderated by age, sex, educational level, or apolipoprotein E genotype.Among 2908 persons with normal cognition (mean [SD] age, 67.4 [12.8] years), amyloid positivity was associated with low memory scores after age 70 years (mean difference in amyloid positive vs negative, 4% [95% CI, 0%-7%] at 72 years and 21% [95% CI, 10%-33%] at 90 years) but was not associated with low MMSE scores (mean difference, 3% [95% CI, -1% to 6%], P = .16). Among 4133 patients with MCI (mean [SD] age, 70.2 [8.5] years), amyloid positivity was associated with low memory (mean difference, 16% [95% CI, 12%-20%], P < .001) and low MMSE (mean difference, 14% [95% CI, 12%-17%], P < .001) scores, and this association decreased with age. Low cognitive scores had limited utility for screening of amyloid positivity in persons with normal cognition and those with MCI. In persons with normal cognition, the age-related increase in low memory score paralleled the age-related increase in amyloid positivity with an intervening period of 10 to 15 years.Although low memory scores are an early marker of amyloid positivity, their value as a screening measure for early AD among persons without dementia is limited.
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| 2. |
- Mattsson, Niklas, et al.
(författare)
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Prevalence of the apolipoprotein E epsilon 4 allele in amyloid beta positive subjects across the spectrum of Alzheimers disease
- 2018
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Ingår i: Alzheimer's & Dementia. - : ELSEVIER SCIENCE INC. - 1552-5260 .- 1552-5279. ; 14:7, s. 913-924
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Apolipoprotein E (APOE) epsilon 4 is the major genetic risk factor for Alzheimers disease (AD), but its prevalence is unclear because earlier studies did not require biomarker evidence of amyloid beta(A beta) pathology. Methods: We included 3451 A beta+ subjects (853 AD-type dementia, 1810 mild cognitive impairment, and 788 cognitively normal). Generalized estimating equation models were used to assess APOE epsilon 4 prevalence in relation to age, sex, education, and geographical location. Results: The APOE epsilon 4 prevalence was 66% in AD-type dementia, 64% in mild cognitive impairment, and 51% in cognitively normal, and it decreased with advancing age in A beta+ cognitively normal and A beta+ mild cognitive impairment (P amp;lt;.05) but not in A beta+ AD dementia (P =.66). The prevalence was highest in Northern Europe but did not vary by sex or education. Discussion: The APOE E4 prevalence in AD was higher than that in previous studies, which did not require presence of A beta pathology. Furthermore, our results highlight disease heterogeneity related to age and geographical location. (C) 2018 the Alzheimers Association. Published by Elsevier Inc. All rights reserved.
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| 3. |
- Mattsson, Niklas, et al.
(författare)
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Prevalence of the apolipoprotein E ε4 allele in amyloid β positive subjects across the spectrum of Alzheimer's disease
- 2018
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Ingår i: Alzheimer's and Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 14:7, s. 913-924
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Apolipoprotein E (APOE) ε4 is the major genetic risk factor for Alzheimer's disease (AD), but its prevalence is unclear because earlier studies did not require biomarker evidence of amyloid β (Aβ) pathology. Methods: We included 3451 Aβ+ subjects (853 AD-type dementia, 1810 mild cognitive impairment, and 788 cognitively normal). Generalized estimating equation models were used to assess APOE ε4 prevalence in relation to age, sex, education, and geographical location. Results: The APOE ε4 prevalence was 66% in AD-type dementia, 64% in mild cognitive impairment, and 51% in cognitively normal, and it decreased with advancing age in Aβ+ cognitively normal and Aβ+ mild cognitive impairment (P <.05) but not in Aβ+ AD dementia (P =.66). The prevalence was highest in Northern Europe but did not vary by sex or education. Discussion: The APOE ε4 prevalence in AD was higher than that in previous studies, which did not require presence of Aβ pathology. Furthermore, our results highlight disease heterogeneity related to age and geographical location.
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| 4. |
- Etminani, Kobra, 1984-, et al.
(författare)
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A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
- 2022
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
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| 5. |
- Soliman, Amira, 1980-, et al.
(författare)
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Adopting transfer learning for neuroimaging : a comparative analysis with a custom 3D convolution neural network model
- 2022
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Ingår i: BMC Medical Informatics and Decision Making. - London : BioMed Central (BMC). - 1472-6947. ; 22, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones. © 2022, The Author(s).
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| 6. |
- Stockbauer, Anna, et al.
(författare)
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Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission
- 2023
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : SPRINGER. - 1619-7070 .- 1619-7089.
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA).Methods FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level.Results Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912).Conclusion Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.
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