| 1. |
- Canal-Garcia, Anna, et al.
(författare)
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Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease
- 2024
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Ingår i: CEREBRAL CORTEX. - 1047-3211 .- 1460-2199.
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Tidskriftsartikel (refereegranskat)abstract
- Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE epsilon 4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.
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| 2. |
- Canal Garcia, Anna
(författare)
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Multimodal and multiscale brain networks : understanding aging, Alzheimer’s disease, and other neurodegenerative disorders
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The human brain can be modeled as a complex network, often referred to as the connectome, where structural and functional connections govern its organization. Several neuroimaging studies have focused on understanding the architecture of healthy brain networks and have shed light on how these networks evolve with age and in the presence of neurodegenerative disorders. Many studies have explored the brain networks in Alzheimer’s disease (AD), the most common type of dementia, using various neuroimaging modalities independently. However, most of these studies ignored the complex and multifactorial nature of AD. The aim of this thesis was to investigate and analyze the brain’s multimodal and multiscale network organization in aging and in AD by using different multilayer brain network analyses and different types of data. Additionally, this research extended its scope to incorporate other dementias, such as Lewy body dementias, allowing for a comparison of these disorders with AD and normal aging. These comparisons were made possible through the application of protein co-expression networks. In Study I, we investigated sex differences in healthy individuals using multimodal brain networks. To do this we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion-weighted imaging (DWI) data from the Human Connectome Project (HCP) to perform multilayer and deep learning analyses. These analyses identified differences between men's and women's underlying brain network organization, showing that the deep-learning analysis with multilayer network metrics (area under the curve, AUC, of 0.81) outperforms the classification using single-layer network measures (AUC of 0.72 for functional networks and 0.70 for anatomical networks). Furthermore, we integrated the multilayer brain networks methodology and neural network models into a software package that is easy to use by researchers with different backgrounds and is also easily expandable for researchers with different levels of programming experience. Then, we used the multilayer brain networks methodology to study the interaction between sex and age on the functional network topology using a large group of people from the UK Biobank (Study II). By incorporating multilayer brain network analyses, we analyzed both positive and negative connections derived from functional correlations, and we obtained important insights into how cognitive abilities, physical health, and even genetic factors differ between men and women as they age. Age and sex were strongly associated with multiplex and multilayer measures such as the multiplex participation coefficient, multilayer clustering, and multilayer global efficiency, accounting for up to 89.1%, 79.9%, and 79.5% of the variance related to age, respectively. These results indicate that incorporating separate layers for positive and negative connections within a complex network framework reveals sensitive insights into age- and sex-related variations that are not detected by traditional metrics. Furthermore, our functional metrics exhibited associations with genes that have previously been linked to processes related to aging. In Study III, we assessed whether multilayer connectome analyses could offer new perspectives on the relationship between amyloid pathology and gray matter atrophy across the AD continuum. Subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were divided into four groups based on cerebrospinal fluid (CSF) amyloid-β (Aβ) biomarker levels and clinical diagnosis. We compared the different groups using weighted and binary multilayer measures that assess the strength of the connections, the modularity, as well as the multiplex segregation and integration of the brain connectomes. Across Aβ-positive (Aβ+) groups, we found widespread increases in the overlapping connectivity strength and decreases in the number of identical connections in both layers. Moreover, the brain modules were reorganized in the mild cognitive impairment (MCI) Aβ+ group and an imbalance in the quantity of couplings between the two layers was found in patients with MCI Aβ+ and AD Aβ+. Using a subsample from the same database, ADNI, we analyzed rs-fMRI data from individuals at preclinical and clinical stages of AD (Study IV). By dividing the time series into different time windows, we built temporal multilayer networks and studied the modular organization across time. We were able to capture the dynamic changes across different AD stages using this temporal multilayer network approach, obtaining outstanding areas under the curve of 0.90, 0.92 and 0.99 in the distinction of controls from preclinical, prodromal, and clinical AD stages, respectively, on top and beyond common risk factors. Our results not only improved the discrimination between various disease stages but, importantly, they also showed that dynamic multilayer functional measures are associated with memory and global cognition in addition to amyloid and tau load derived from positron emission tomography. These results highlight the potential of dynamic multilayer functional connectivity measures as functional biomarkers of AD progression. In Study V, we used in-depth quantitative proteomics to compare post-mortem brains from three key brain regions (prefrontal cortex, cingulate cortex, and the parietal cortex) directly related to the disease mechanisms of AD, Parkinson’s disease with dementia (PDD), dementia with Lewy bodies (DLB) in prospectively followed patients and older adults without dementia. We used covariance weighted networks to find modules of protein sets to further understand altered pathways in these dementias and their implications for prognostic and diagnostic purposes. In conclusion, this thesis explored the complex world of brain networks and offered insightful information about how age, sex, and AD influence these networks. We have improved our understanding of how the brain is organized in different imaging modalities and different time scales, as well as developing software tools to make this methodology available to more researchers. Additionally, we assessed the connections among various proteins in different areas of the brain in relation to health, Alzheimer's disease, and Lewy body dementias. This work contributes to the collective effort of unraveling the mysteries of the human brain organization and offers a foundation for future research to understand brain networks in health and disease.
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| 4. |
- Mijalkov, Mite, et al.
(författare)
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Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants
- 2023
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Ingår i: Network Neuroscience. - : MIT Press. - 2472-1751. ; 7:1, s. 351-376
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Tidskriftsartikel (refereegranskat)abstract
- Aging is a major risk factor for cardiovascular and neurodegenerative disorders, with considerable societal and economic implications. Healthy aging is accompanied by changes in functional connectivity between and within resting-state functional networks, which have been associated with cognitive decline. However, there is no consensus on the impact of sex on these age-related functional trajectories. Here, we show that multilayer measures provide crucial information on the interaction between sex and age on network topology, allowing for better assessment of cognitive, structural, and cardiovascular risk factors that have been shown to differ between men and women, as well as providing additional insights into the genetic influences on changes in functional connectivity that occur during aging. In a large crosssectional sample of 37,543 individuals from the UK Biobank cohort, we demonstrate that such multilayer measures that capture the relationship between positive and negative connections are more sensitive to sex-related changes in the whole-brain connectivity patterns and their topological architecture throughout aging, when compared to standard connectivity and topological measures. Our findings indicate that multilayer measures contain previously unknown information on the relationship between sex and age, which opens up new avenues for research into functional brain connectivity in aging.
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