Recent findings suggest that Alzheimer's disease (AD) is a disconnection syndrome characterized by abnormalities in large-scale networks. However, the alterations that occur in network topology during the prodromal stages of AD, particularly in patients with stable mild cognitive impairment (MCI) and those that show a slow or faster progression to dementia, are still poorly understood. In this study, we used graph theory to assess the organization of structural MRI networks in stable MCI (sMCI) subjects, late MCI converters (lMCIc), early MCI converters (eMCIc), and AD patients from 2 large multicenter cohorts: ADNI and AddNeuroMed. Our findings showed an abnormal global network organization in all patient groups, as reflected by an increased path length, reduced transitivity, and increased modularity compared with controls. In addition, lMCIc, eMCIc, and AD patients showed a decreased path length and mean clustering compared with the sMCI group. At the local level, there were nodal clustering decreases mostly in AD patients, while the nodal closeness centrality detected abnormalities across all patient groups, showing overlapping changes in the hippocampi and amygdala and nonoverlapping changes in parietal, entorhinal, and orbitofrontal regions. These findings suggest that the prodromal and clinical stages of AD are associated with an abnormal network topology.
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http://dx.doi.org/10.1093/cercor/bhw128 | DOI Listing |
Nano Lett
January 2025
Institute of Experimental and Applied Physics, Kiel University, Leibnizstr. 11-19, Kiel 24098, Germany.
Topological plasmonics combines principles of topology and plasmonics to provide new methods for controlling light, analogous to topological edge states in photonics. However, designing such topological states remains challenging due to the complexity of the high-dimensional design space. We present a novel method that uses supervised, physics-informed deep learning and surrogate modeling to design topological devices for desired wavelengths.
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January 2025
International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Bunkyo Ku, Tokyo 113 8654, Japan.
We investigate the aging transition in networks of excitable and self-oscillatory units as the fraction of inherently excitable units increases. Two network topologies are considered: a scale-free network with weighted pairwise interactions and a two-dimensional simplicial complex with weighted scale-free pairwise and triadic interactions. Without triadic interactions, the aging transition from collective oscillations to oscillation death (inhomogeneous stationary states) can occur either suddenly or through an intermediate state of partial oscillation.
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January 2025
Department of Management Science and Technology, Tohoku University, Sendai 980-8579, Japan.
Complex network approaches have been emerging as an analysis tool for dynamical systems. Different reconstruction methods from time series have been shown to reveal complicated behaviors that can be quantified from the network's topology. Directed recurrence networks have recently been suggested as one such method, complementing the already successful recurrence networks and expanding the applications of recurrence analysis.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Amsterdam UMC, Amsterdam, Amsterdam, Netherlands.
Background: Amyotrophic lateral sclerosis (ALS) with only motor impairment (ALS-pure motor) and the behavioral variant of frontotemporal dementia (bvFTD) are hypothesized to be the extreme ends of the ALS-bvFTD spectrum. This spectrum also encompasses ALS patients with mild to severe cognitive impairment (ALSci) and/or behavioral impairment (ALSbi), including ALS with concomitant bvFTD. In a previous study, using magnetoencephalography (MEG), in early symptomatic ALS patients we showed resting-state functional connectivity changes in frontal, limbic and subcortical regions that overlap considerably with bvFTD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland, OH, USA.
Background: The emerging tools of protein-protein interactome network offer a platform to explore not only the molecular complexity of human diseases, but also to identify risk genes and drug targets. Integration of the genome, transcriptome, proteome, and the interactome networks are essential for such identification, including Alzheimer's disease (AD), Parkinson disease (PD), and Amyotrophic lateral sclerosis (ALS) METHOD: In this study, we performed multi-modal analyses of cross-species protein interactome networks and human brain functional genomics data to identify risk genes and drug targets for neurodegenerative diseases. We presented a multi-view topology-based deep learning framework to identify disease-associated genes for cross-species interactome (TAG-X).
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