| 11. |
- Altomare, Daniele, et al.
(författare)
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Applying the ATN scheme in a memory clinic population : The ABIDE project
- 2019
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Ingår i: Neurology. - 1526-632X. ; 93:17, s. 1635-1646
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: To apply the ATN scheme to memory clinic patients, to assess whether it discriminates patient populations with specific features. METHODS: We included 305 memory clinic patients (33% subjective cognitive decline [SCD]: 60 ± 9 years, 61% M; 19% mild cognitive impairment [MCI]: 68 ± 9 years, 68% M; 48% dementia: 66 ± 10 years, 58% M) classified for positivity (±) of amyloid (A) ([18F]Florbetaben PET), tau (T) (CSF p-tau), and neurodegeneration (N) (medial temporal lobe atrophy). We assessed ATN profiles' demographic, clinical, and cognitive features at baseline, and cognitive decline over time. RESULTS: The proportion of A+T+N+ patients increased with syndrome severity (from 1% in SCD to 14% in MCI and 35% in dementia), while the opposite was true for A-T-N- (from 48% to 19% and 6%). Compared to A-T-N-, patients with the Alzheimer disease profiles (A+T+N- and A+T+N+) were older (both p < 0.05) and had a higher prevalence of APOE ε4 (both p < 0.05) and lower Mini-Mental State Examination (MMSE) (both p < 0.05), memory (both p < 0.05), and visuospatial abilities (both p < 0.05) at baseline. Non-Alzheimer profiles A-T-N+ and A-T+N+ showed more severe white matter hyperintensities (both p < 0.05) and worse language performance (both p < 0.05) than A-T-N-. A linear mixed model showed faster decline on MMSE over time in A+T+N- and A+T+N+ (p = 0.059 and p < 0.001 vs A-T-N-), attributable mainly to patients without dementia. CONCLUSIONS: The ATN scheme identified different biomarker profiles with overlapping baseline features and patterns of cognitive decline. The large number of profiles, which may have different implications in patients with vs without dementia, poses a challenge to the application of the ATN scheme.
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| 12. |
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| 13. |
- van Engelen, Marie Paule E., et al.
(författare)
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The bvFTD phenocopy syndrome : a case study supported by repeated MRI, [18F]FDG-PET and pathological assessment
- 2021
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Ingår i: Neurocase. - : Informa UK Limited. - 1355-4794 .- 1465-3656. ; 27:2, s. 181-189
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Tidskriftsartikel (refereegranskat)abstract
- A clinical syndrome with neuropsychiatric features of bvFTD without neuroimaging abnormalities and a lack of decline is a phenocopy of bvFTD (phFTD). Growing evidence suggests that psychological, psychiatric and environmental factors underlie phFTD. We describe a patient diagnosed with bvFTD prior to the revision of the diagnostic guidelines of FTD. Repeated neuroimaging was normal and there was no FTD pathology at autopsy, rejecting the diagnosis. We hypothesize on etiological factors that on hindsight might have played a role. This case report contributes to the understanding of phFTD and adds to the sparse literature of the postmortem assessment of phFTD.
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| 14. |
- van Engelen, Marie Paule E., et al.
(författare)
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Altered brain metabolism in frontotemporal dementia and psychiatric disorders : involvement of the anterior cingulate cortex
- 2023
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Ingår i: EJNMMI Research. - 2191-219X. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Behavioural symptoms and frontotemporal hypometabolism overlap between behavioural variant of frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPD), hampering diagnostic distinction. Voxel-wise comparisons of brain metabolism might identify specific frontotemporal-(hypo)metabolic regions between bvFTD and PPD. We investigated brain metabolism in bvFTD and PPD and its relationship with behavioural symptoms, social cognition, severity of depressive symptoms and cognitive functioning. Results: Compared to controls, bvFTD showed decreased metabolism in the dorsal anterior cingulate cortex (dACC) (p < 0.001), orbitofrontal cortex (OFC), temporal pole, dorsolateral prefrontal cortex (dlPFC) and caudate, whereas PPD showed no hypometabolism. Compared to PPD, bvFTD showed decreased metabolism in the dACC (p < 0.001, p < 0.05FWE), insula, Broca’s area, caudate, thalamus, OFC and temporal cortex (p < 0.001), whereas PPD showed decreased metabolism in the motor cortex (p < 0.001). Across bvFTD and PPD, decreased metabolism in the temporal cortex (p < 0.001, p < 0.05FWE), dACC and frontal cortex was associated with worse social cognition. Decreased metabolism in the dlPFC was associated with compulsiveness (p < 0.001). Across bvFTD, PPD and controls, decreased metabolism in the PFC and motor cortex was associated with executive dysfunctioning (p < 0.001). Conclusions: Our findings indicate subtle but distinct metabolic patterns in bvFTD and PPD, most strongly in the dACC. The degree of frontotemporal and cingulate hypometabolism was related to impaired social cognition, compulsiveness and executive dysfunctioning. Our findings suggest that the dACC might be an important region to differentiate between bvFTD and PPD but needs further validation.
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| 15. |
- Bos, Isabelle, et al.
(författare)
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The frequency and influence of dementia risk factors in prodromal Alzheimer's disease
- 2017
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Ingår i: Neurobiology of Aging. - : Elsevier. - 0197-4580 .- 1558-1497. ; 56, s. 33-40
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Tidskriftsartikel (refereegranskat)abstract
- We investigated whether dementia risk factors were associated with prodromal Alzheimer's disease (AD) according to the International Working Group-2 and National Institute of Aging-Alzheimer's Association criteria, and with cognitive decline. A total of 1394 subjects with mild cognitive impairment from 14 different studies were classified according to these research criteria, based on cognitive performance and biomarkers. We compared the frequency of 10 risk factors between the subgroups, and used Cox-regression to examine the effect of risk factors on cognitive decline. Depression, obesity, and hypercholesterolemia occurred more often in individuals with low-AD-likelihood, compared with those with a high-AD-likelihood. Only alcohol use increased the risk of cognitive decline, regardless of AD pathology. These results suggest that traditional risk factors for AD are not associated with prodromal AD or with progression to dementia, among subjects with mild cognitive impairment. Future studies should validate these findings and determine whether risk factors might be of influence at an earlier stage (i.e., preclinical) of AD.
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| 16. |
- 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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| 17. |
- 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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