| 1. |
- Abdelnour, Carla, et al.
(författare)
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Parsing heterogeneity within dementia with Lewy bodies using clustering of biological, clinical, and demographic data
- 2022
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Ingår i: Alzheimer's Research & Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer's disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features.Methods: We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions.Results: We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-beta and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores.Conclusions: This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials.
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| 2. |
- 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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| 3. |
- Gonzalez, Maria C, et al.
(författare)
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Association of Plasma p-tau181 and p-tau231 Concentrations With Cognitive Decline in Patients With Probable Dementia With Lewy Bodies.
- 2022
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Ingår i: JAMA neurology. - : American Medical Association (AMA). - 2168-6157 .- 2168-6149. ; 79:1, s. 32-37
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Tidskriftsartikel (refereegranskat)abstract
- Plasma phosphorylated tau (p-tau) has proven to be an accurate biomarker for Alzheimer disease (AD) pathologic characteristics, offering a less expensive and less invasive alternative to cerebrospinal fluid (CSF) and positron emission tomography biomarkers for amyloid-β and tau. Alzheimer disease comorbid pathologic characteristics are common and are associated with more rapid cognitive decline in patients with dementia with Lewy bodies (DLB); therefore, it is anticipated that plasma p-tau concentrations may have utility in assessing cognitive impairment in individuals with this disorder.To measure the concentrations of plasma p-tau (p-tau181 and p-tau231) and evaluate their associations with cognitive decline in individuals with probable DLB.This multicenter longitudinal cohort study included participants from the European-DLB (E-DLB) Consortium cohort enrolled at 10 centers with harmonized diagnostic procedures from January 1, 2002, to December 31, 2020, with up to 5 years of follow-up. A total of 1122 participants with plasma samples were available. Participants with acute delirium or terminal illness and patients with other previous major psychiatric or neurologic disorders were excluded, leaving a cohort of 987 clinically diagnosed participants with probable DLB (n=371), Parkinson disease (n=204), AD (n=207), as well as healthy controls (HCs) (n=205).The main outcome was plasma p-tau181 and p-tau231 levels measured with in-house single molecule array assays. The Mini-Mental State Examination (MMSE) was used to measure cognition.Among this cohort of 987 patients (512 men [51.9%]; mean [SD] age, 70.0 [8.8] years), patients with DLB did not differ significantly regarding age, sex, or years of education from those in the AD group, but the DLB group was older than the HC group and included more men than the AD and HC groups. Baseline concentrations of plasma p-tau181 and p-tau231 in patients with DLB were significantly higher than those in the HC group but lower than in the AD group and similar to the Parkinson disease group. Higher plasma concentrations of both p-tau markers were found in a subgroup of patients with DLB with abnormal CSF amyloid-β42 levels compared with those with normal levels (difference in the groups in p-tau181, -3.61 pg/mL; 95% CI, -5.43 to -1.79 pg/mL; P=.049; difference in the groups in p-tau231, -2.51 pg/mL; 95% CI, -3.63 to -1.39 pg/mL; P=.02). There was no difference between p-tau181 level and p-tau231 level across confirmed AD pathologic characteristcs based on reduced Aβ42 level in CSF in individuals with DLB. In DLB, a significant association was found between higher plasma p-tau181 and p-tau231 levels and lower MMSE scores at baseline (for p-tau181, -0.092 MMSE points; 95% CI, -0.12 to -0.06 MMSE points; P=.001; for p-tau231, -0.16 MMSE points; 95% CI, -0.21 to -0.12 MMSE points; P<.001), as well as more rapid MMSE decline over time. Plasma p-tau181 level was associated with a decrease of -0.094 MMSE points per year (95% CI, -0.144 to -0.052 MMSE points; P=.02), whereas plasma p-tau231 level was associated with an annual decrease of -0.130 MMSE points (95% CI, -0.201 to -0.071 MMSE points; P=.02), after adjusting for sex and age.This study suggests that plasma p-tau181 and p-tau231 levels may be used as cost-effective and accessible biomarkers to assess cognitive decline in individuals with DLB.
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| 4. |
- 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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| 5. |
- Ulugut, Hulya, et al.
(författare)
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The natural history of primary progressive aphasia : beyond aphasia
- 2022
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Ingår i: Journal of Neurology. - : Springer Science and Business Media LLC. - 0340-5354 .- 1432-1459. ; 269:3, s. 1375-1385
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Primary progressive aphasia (PPA) is divided into three prototypical subtypes that are all characterized by their single core symptom of aphasia. Although later in their course, other cognitive, behavioral, and motor domains may become involved, little is known about the progression profile of each subtype relative to the other subtypes. Methods: In this longitudinal retrospective cohort study, based on the recent biomarker-supported diagnostic criteria, 24 subjects diagnosed with semantic variant (svPPA), 22 with non-fluent variant (nfvPPA), and 18 with logopenic variant (lvPPA) were collected and followed up for 1–6 years. Symptom distribution, cognitive test and neuropsychiatric inventory scores, and progression into another syndrome were assessed. Results: Over time, lvPPA progressed with broader language problems (PPA-extended) and nfvPPA progressed to mutism, whereas semantic impairment remained the major problem in svPPA. Apart from linguistic problems, svPPA developed pronounced behavioral disturbances, whereas lvPPA exhibited a greater cognitive decline. By contrast, in nfvPPA motor deficits were more common. Furthermore, within 5 years (IQR = 2.5) after clinical onset, 65.6% of the patients additionally fulfilled the clinical criteria for another neurodegenerative syndrome (PPA-plus). Fourteen out of 24 (58%) svPPA patients additionally met the diagnostic criteria of behavioral variant frontotemporal dementia (5.1 years, IQR = 1.1), whereas the clinical features of 15/18 (83%) lvPPA patients were consistent with Alzheimer disease dementia (4.5 years IQR = 3.4). Furthermore, 12/22 (54%) of the subjects with the nfvPPA progressed to meet the diagnostic criteria of corticobasal syndrome, progressive supranuclear palsy, or motor neuron disease (5.1 years IQR = 3.4). Discussion: Despite aphasia being the initial and unique hallmark of the syndrome, our longitudinal results showed that PPA is not a language limited disorder and progression differs widely for each subtype, both with respect to the nature of symptoms and disease duration.
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