| 1. |
- Conceicao, Pedro, et al.
(författare)
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An application of neighbourhoods in digraphs to the classification of binary dynamics
- 2022
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Ingår i: NETWORK NEUROSCIENCE. - : MIT PRESS. - 2472-1751. ; 6:2, s. 528-551
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Tidskriftsartikel (refereegranskat)abstract
- A binary state on a graph means an assignment of binary values to its vertices. A time-dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a nonbiological random graph with similar density. Author Summary We explore the mathematical concept of a closed neighbourhood in a digraph in relation to classifying binary dynamics on a digraph, with particular emphasis on dynamics on a neuronal network. Using methodology based on selecting neighbourhoods and vectorising them by combinatorial and topological parameters, we experimented with a dataset implemented on the Blue Brain Project reconstruction of a neocortical column, and on an artificial neural network with random underlying graph implemented on the NEST simulator. In both cases the outcome was run through a support vector machine algorithm reaching classification accuracy of up to 88% for the Blue Brain Project data and up to 81% for the NEST data. This work is open to generalisation to other types of networks and the dynamics on them.
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| 2. |
- Fanton, S., et al.
(författare)
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NetPlotBrain: A Python package for visualizing networks and brains
- 2023
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Ingår i: Network Neuroscience. - : MIT Press. - 2472-1751. ; 7:2, s. 461-477
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Tidskriftsartikel (refereegranskat)abstract
- Author Summary NetPlotBrain is a Python package to easily create network visualizations on a brain and view brain anatomy. NetPlotBrain is integrated with TemplateFlow and popular Python packages, the former facilitating the selection of the appropriate template or atlas from the available options and the latter providing the user with easy customization and fine-tuning. Visualizations of networks are complex since they are multidimensional and generally convey large amounts of information. The layout of the visualization can communicate either network properties or spatial properties of the network. Generating such figures to effectively convey information and be accurate can be difficult and time-consuming, and it can require expert knowledge. Here, we introduce NetPlotBrain (short for network plots onto brains), a Python package for Python 3.9+. The package offers several advantages. First, NetPlotBrain provides a high-level interface to easily highlight and customize results of interest. Second, it presents a solution to promote accurate plots through its integration with TemplateFlow. Third, it integrates with other Python software, allowing for easy integration to include networks from NetworkX or implementations of network-based statistics. In sum, NetPlotBrain is a versatile but easy to use package designed to produce high-quality network figures while integrating with open research software for neuroimaging and network theory.
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| 3. |
- Kaboodvand, N, et al.
(författare)
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Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates
- 2019
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Ingår i: Network neuroscience (Cambridge, Mass.). - : MIT Press - Journals. - 2472-1751. ; 3:4, s. 1094-1120
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Tidskriftsartikel (refereegranskat)abstract
- Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in coactivation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space.
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| 4. |
- Koba, Cemal, et al.
(författare)
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Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity
- 2021
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Ingår i: Network Neuroscience. - : MIT Press - Journals. - 2472-1751. ; 5:2, s. 451-476
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Tidskriftsartikel (refereegranskat)abstract
- During wakeful rest, individuals make small eye movements during fixation. We examined how these endogenously driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye tracking. The eye-tracking data indicated minor movements during rest, which correlated modestly with RS BOLD data. However, eye-tracking data correlated well with echo-planar imaging time series sampled from the area of the eye-orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. Further analyses showed that EO-EPI data were correlated with activity in an extensive motor and sensorimotor network, including components of the dorsal attention network and the frontal eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensorimotor and visual areas. It also produced networks with higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations or as a function of different experimental conditions.
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| 5. |
- Lu, Han, et al.
(författare)
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Network remodeling induced by transcranial brain stimulation: A computational model of tDCS-triggered cell assembly formation
- 2019
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Ingår i: Network Neuroscience. - : MIT Press - Journals. - 2472-1751. ; 3:4, s. 924-943
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Tidskriftsartikel (refereegranskat)abstract
- Transcranial direct current stimulation (tDCS) is a variant of noninvasive neuromodulation, which promises treatment for brain diseases like major depressive disorder. In experiments, long-lasting aftereffects were observed, suggesting that persistent plastic changes are induced. The mechanism underlying the emergence of lasting aftereffects, however, remains elusive. Here we propose a model, which assumes that tDCS triggers a homeostatic response of the network involving growth and decay of synapses. The cortical tissue exposed to tDCS is conceived as a recurrent network of excitatory and inhibitory neurons, with synapses subject to homeostatically regulated structural plasticity. We systematically tested various aspects of stimulation, including electrode size and montage, as well as stimulation intensity and duration. Our results suggest that transcranial stimulation perturbs the homeostatic equilibrium and leads to a pronounced growth response of the network. The stimulated population eventually eliminates excitatory synapses with the unstimulated population, and new synapses among stimulated neurons are grown to form a cell assembly. Strong focal stimulation tends to enhance the connectivity within new cell assemblies, and repetitive stimulation with well-chosen duty cycles can increase the impact of stimulation even further. One long-term goal of our work is to help in optimizing the use of tDCS in clinical applications.
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| 6. |
- Lurie, DJ, et al.
(författare)
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Questions and controversies in the study of time-varying functional connectivity in resting fMRI
- 2020
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Ingår i: Network neuroscience (Cambridge, Mass.). - : MIT Press - Journals. - 2472-1751. ; 4:1, s. 30-69
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Tidskriftsartikel (refereegranskat)abstract
- The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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| 7. |
- 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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| 8. |
- St-Onge, Frédéric, et al.
(författare)
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Functional connectome fingerprinting across the lifespan
- 2023
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Ingår i: Network Neuroscience. - 2472-1751. ; 7:3, s. 1206-1227
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Tidskriftsartikel (refereegranskat)abstract
- Systematic changes have been observed in the functional architecture of the human brain with advancing age. However, functional connectivity (FC) is also a powerful feature to detect unique “connectome fingerprints,” allowing identification of individuals among their peers. Although fingerprinting has been robustly observed in samples of young adults, the reliability of this approach has not been demonstrated across the lifespan. We applied the fingerprinting framework to the Cambridge Centre for Ageing and Neuroscience cohort (n = 483 aged 18 to 89 years). We found that individuals are “fingerprintable” (i.e., identifiable) across independent functional MRI scans throughout the lifespan. We observed a U-shape distribution in the strength of “self-identifiability” (within-individual correlation across modalities), and “others-identifiability” (between-individual correlation across modalities), with a decrease from early adulthood into middle age, before improving in older age. FC edges contributing to self-identifiability were not restricted to specific brain networks and were different between individuals across the lifespan sample. Self-identifiability was additionally associated with regional brain volume. These findings indicate that individual participant-level identification is preserved across the lifespan despite the fact that its components are changing nonlinearly.
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| 9. |
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