| 1. |
- Bauckneht, Matteo, et al.
(författare)
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Associations among education, age, and the dementia with Lewy bodies (DLB) metabolic pattern: A European-DLB consortium project
- 2021
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Ingår i: Alzheimer's & Dementia. - : WILEY. - 1552-5260 .- 1552-5279. ; 17:8, s. 1277-1286
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Tidskriftsartikel (refereegranskat)abstract
- Introduction We assessed the influence of education as a proxy of cognitive reserve and age on the dementia with Lewy bodies (DLB) metabolic pattern. Methods Brain 18F-fluorodeoxyglucose positron emission tomography and clinical/demographic information were available in 169 probable DLB patients included in the European DLB-consortium database. Principal component analysis identified brain regions relevant to local data variance. A linear regression model was applied to generate age- and education-sensitive maps corrected for Mini-Mental State Examination score, sex (and either education or age). Results Age negatively covaried with metabolism in bilateral middle and superior frontal cortex, anterior and posterior cingulate, reducing the expression of the DLB-typical cingulate island sign (CIS). Education negatively covaried with metabolism in the left inferior parietal cortex and precuneus (making the CIS more prominent). Discussion These findings point out the importance of tailoring interpretation of DLB biomarkers considering the concomitant effect of individual, non-disease-related variables such as age and cognitive reserve.
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| 2. |
- Duits, Flora H., et al.
(författare)
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The cerebrospinal fluid "Alzheimer profile": Easily said, but what does it mean?
- 2014
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Ingår i: Alzheimer's & Dementia. - : Elsevier. - 1552-5260 .- 1552-5279. ; 10:6, s. 713-723
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Tidskriftsartikel (refereegranskat)abstract
- Background: We aimed to identify the most useful definition of the "cerebrospinal fluid Alzheimer profile," based on amyloid-beta(1-42) (A beta(42)), total tau, and phosphorylated tau (p-tau), for diagnosis and prognosis of Alzheimers disease (AD). Methods: We constructed eight Alzheimer profiles with previously published combinations, including regression formulas and simple ratios. We compared their diagnostic accuracy and ability to predict dementia due to AD in 1385 patients from the Amsterdam Dementia Cohort. Results were validated in an independent cohort (n = 1442). Results: Combinations outperformed individual biomarkers. Based on the sensitivity of the best performing regression formulas, cutoffs were chosen at 0.52 for the tau/A beta(42) ratio and 0.08 for the p-tau/A beta(42) ratio. Ratios performed similar to formulas (sensitivity, 91%-93%; specificity, 81%-84%). The same combinations best predicted cognitive decline in mild cognitive impairment patients. Validation confirmed these results, especially regarding the tau/A beta(42) ratio. Conclusions: A tau/A beta(42) ratio of greater than0.52 constitutes a robust cerebrospinal fluid Alzheimer profile. We recommend using this ratio to combine biomarkers.
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| 3. |
- 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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| 4. |
- Gurholt, Tiril P., et al.
(författare)
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Linking sarcopenia, brain structure and cognitive performance: a large-scale UK Biobank study
- 2024
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Ingår i: Brain Communications. - : OXFORD UNIV PRESS. - 2632-1297. ; 6:2
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Tidskriftsartikel (refereegranskat)abstract
- Sarcopenia refers to age-related loss of muscle mass and function and is related to impaired somatic and brain health, including cognitive decline and Alzheimer's disease. However, the relationships between sarcopenia, brain structure and cognition are poorly understood. Here, we investigate the associations between sarcopenic traits, brain structure and cognitive performance. We included 33 709 UK Biobank participants (54.2% female; age range 44-82 years) with structural and diffusion magnetic resonance imaging, thigh muscle fat infiltration (n = 30 561) from whole-body magnetic resonance imaging (muscle quality indicator) and general cognitive performance as indicated by the first principal component of a principal component analysis across multiple cognitive tests (n = 22 530). Of these, 1703 participants qualified for probable sarcopenia based on low handgrip strength, and we assigned the remaining 32 006 participants to the non-sarcopenia group. We used multiple linear regression to test how sarcopenic traits (probable sarcopenia versus non-sarcopenia and percentage of thigh muscle fat infiltration) relate to cognitive performance and brain structure (cortical thickness and area, white matter fractional anisotropy and deep and lower brain volumes). Next, we used structural equation modelling to test whether brain structure mediated the association between sarcopenic and cognitive traits. We adjusted all statistical analyses for confounders. We show that sarcopenic traits (probable sarcopenia versus non-sarcopenia and muscle fat infiltration) are significantly associated with lower cognitive performance and various brain magnetic resonance imaging measures. In probable sarcopenia, for the included brain regions, we observed widespread significant lower white matter fractional anisotropy (77.1% of tracts), predominantly lower regional brain volumes (61.3% of volumes) and thinner cortical thickness (37.9% of parcellations), with |r| effect sizes in (0.02, 0.06) and P-values in (0.0002, 4.2e(-29)). In contrast, we observed significant associations between higher muscle fat infiltration and widespread thinner cortical thickness (76.5% of parcellations), lower white matter fractional anisotropy (62.5% of tracts) and predominantly lower brain volumes (35.5% of volumes), with |r| effect sizes in (0.02, 0.07) and P-values in (0.0002, 1.9e(-31)). The regions showing the most significant effect sizes across the cortex, white matter and volumes were of the sensorimotor system. Structural equation modelling analysis revealed that sensorimotor brain regions mediate the link between sarcopenic and cognitive traits [probable sarcopenia: P-values in (0.0001, 1.0e-11); muscle fat infiltration: P-values in (7.7e(-05), 1.7e(-12))]. Our findings show significant associations between sarcopenic traits, brain structure and cognitive performance in a middle-aged and older adult population. Mediation analyses suggest that regional brain structure mediates the association between sarcopenic and cognitive traits, with potential implications for dementia development and prevention.
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| 5. |
- Huber, Maria, et al.
(författare)
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Metabolic correlates of dopaminergic loss in dementia with lewy bodies
- 2020
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Ingår i: Movement Disorders. - : WILEY. - 0885-3185 .- 1531-8257. ; 35, s. 595-605
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Tidskriftsartikel (refereegranskat)abstract
- Background Striatal dopamine deficiency and metabolic changes are well-known phenomena in dementia with Lewy bodies and can be quantified in vivo by I-123-Ioflupane brain single-photon emission computed tomography of dopamine transporter and F-18-fluorodesoxyglucose PET. However, the linkage between both biomarkers is ill-understood. Objective We used the hitherto largest study cohort of combined imaging from the European consortium to elucidate the role of both biomarkers in the pathophysiological course of dementia with Lewy bodies. Methods We compared striatal dopamine deficiency and glucose metabolism of 84 dementia with Lewy body patients and comparable healthy controls. After normalization of data, we tested their correlation by region-of-interest-based and voxel-based methods, controlled for study center, age, sex, education, and current cognitive impairment. Metabolic connectivity was analyzed by inter-region coefficients stratified by dopamine deficiency and compared to healthy controls. Results There was an inverse relationship between striatal dopamine availability and relative glucose hypermetabolism, pronounced in the basal ganglia and in limbic regions. With increasing dopamine deficiency, metabolic connectivity showed strong deteriorations in distinct brain regions implicated in disease symptoms, with greatest disruptions in the basal ganglia and limbic system, coincident with the pattern of relative hypermetabolism. Conclusions Relative glucose hypermetabolism and disturbed metabolic connectivity of limbic and basal ganglia circuits are metabolic correlates of dopamine deficiency in dementia with Lewy bodies. Identification of specific metabolic network alterations in patients with early dopamine deficiency may serve as an additional supporting biomarker for timely diagnosis of dementia with Lewy bodies. (c) 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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| 6. |
- Mattsson, Niklas, 1979, et al.
(författare)
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CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment.
- 2009
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Ingår i: JAMA : the journal of the American Medical Association. - : American Medical Association (AMA). - 1538-3598 .- 0098-7484. ; 302:4, s. 385-93
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Tidskriftsartikel (refereegranskat)abstract
- CONTEXT: Small single-center studies have shown that cerebrospinal fluid (CSF) biomarkers may be useful to identify incipient Alzheimer disease (AD) in patients with mild cognitive impairment (MCI), but large-scale multicenter studies have not been conducted. OBJECTIVE: To determine the diagnostic accuracy of CSF beta-amyloid(1-42) (Abeta42), total tau protein (T-tau), and tau phosphorylated at position threonine 181 (P-tau) for predicting incipient AD in patients with MCI. DESIGN, SETTING, AND PARTICIPANTS: The study had 2 parts: a cross-sectional study involving patients with AD and controls to identify cut points, followed by a prospective cohort study involving patients with MCI, conducted 1990-2007. A total of 750 individuals with MCI, 529 with AD, and 304 controls were recruited by 12 centers in Europe and the United States. Individuals with MCI were followed up for at least 2 years or until symptoms had progressed to clinical dementia. MAIN OUTCOME MEASURES: Sensitivity, specificity, positive and negative likelihood ratios (LRs) of CSF Abeta42, T-tau, and P-tau for identifying incipient AD. RESULTS: During follow-up, 271 participants with MCI were diagnosed with AD and 59 with other dementias. The Abeta42 assay in particular had considerable intersite variability. Patients who developed AD had lower median Abeta42 (356; range, 96-1075 ng/L) and higher P-tau (81; range, 15-183 ng/L) and T-tau (582; range, 83-2174 ng/L) levels than MCI patients who did not develop AD during follow-up (579; range, 121-1420 ng/L for Abeta42; 53; range, 15-163 ng/L for P-tau; and 294; range, 31-2483 ng/L for T-tau, P < .001). The area under the receiver operating characteristic curve was 0.78 (95% confidence interval [CI], 0.75-0.82) for Abeta42, 0.76 (95% CI, 0.72-0.80) for P-tau, and 0.79 (95% CI, 0.76-0.83) for T-tau. Cut-offs with sensitivity set to 85% were defined in the AD and control groups and tested in the MCI group, where the combination of Abeta42/P-tau ratio and T-tau identified incipient AD with a sensitivity of 83% (95% CI, 78%-88%), specificity 72% (95% CI, 68%-76%), positive LR, 3.0 (95% CI, 2.5-3.4), and negative LR, 0.24 (95% CI, 0.21-0.28). The positive predictive value was 62% and the negative predictive value was 88%. CONCLUSIONS: This multicenter study found that CSF Abeta42, T-tau, and P-tau identify incipient AD with good accuracy, but less accurately than reported from single-center studies. Intersite assay variability highlights a need for standardization of analytical techniques and clinical procedures.
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| 7. |
- 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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| 8. |
- Morbelli, Silvia, et al.
(författare)
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Metabolic patterns across core features in dementia with lewy bodies
- 2019
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Ingår i: Annals of Neurology. - : John Wiley & Sons. - 0364-5134 .- 1531-8249. ; 85:5, s. 715-725
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Tidskriftsartikel (refereegranskat)abstract
- ObjectiveTo identify brain regions whose metabolic impairment contributes to dementia with Lewy bodies (DLB) clinical core features expression and to assess the influence of severity of global cognitive impairment on the DLB hypometabolic pattern.MethodsBrain fluorodeoxyglucose positron emission tomography and information on core features were available in 171 patients belonging to the imaging repository of the European DLB Consortium. Principal component analysis was applied to identify brain regions relevant to the local data variance. A linear regression model was applied to generate core‐feature–specific patterns controlling for the main confounding variables (Mini‐Mental State Examination [MMSE], age, education, gender, and center). Regression analysis to the locally normalized intensities was performed to generate an MMSE‐sensitive map.ResultsParkinsonism negatively covaried with bilateral parietal, precuneus, and anterior cingulate metabolism; visual hallucinations (VH) with bilateral dorsolateral–frontal cortex, posterior cingulate, and parietal metabolism; and rapid eye movement sleep behavior disorder (RBD) with bilateral parieto‐occipital cortex, precuneus, and ventrolateral–frontal metabolism. VH and RBD shared a positive covariance with metabolism in the medial temporal lobe, cerebellum, brainstem, basal ganglia, thalami, and orbitofrontal and sensorimotor cortex. Cognitive fluctuations negatively covaried with occipital metabolism and positively with parietal lobe metabolism. MMSE positively covaried with metabolism in the left superior frontal gyrus, bilateral–parietal cortex, and left precuneus, and negatively with metabolism in the insula, medial frontal gyrus, hippocampus in the left hemisphere, and right cerebellum.InterpretationRegions of more preserved metabolism are relatively consistent across the variegate DLB spectrum. By contrast, core features were associated with more prominent hypometabolism in specific regions, thus suggesting a close clinical–imaging correlation, reflecting the interplay between topography of neurodegeneration and clinical presentation in DLB patients. Ann Neurol 2019;85:715–725
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| 9. |
- 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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| 10. |
- 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
- 2024
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : SPRINGER. - 1619-7070 .- 1619-7089. ; 51:4, s. 1023-1034
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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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