20,479 research outputs found
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Multi-view graph embedding has become a widely studied problem in the area of
graph learning. Most of the existing works on multi-view graph embedding aim to
find a shared common node embedding across all the views of the graph by
combining the different views in a specific way. Hub detection, as another
essential topic in graph mining has also drawn extensive attentions in recent
years, especially in the context of brain network analysis. Both the graph
embedding and hub detection relate to the node clustering structure of graphs.
The multi-view graph embedding usually implies the node clustering structure of
the graph based on the multiple views, while the hubs are the boundary-spanning
nodes across different node clusters in the graph and thus may potentially
influence the clustering structure of the graph. However, none of the existing
works in multi-view graph embedding considered the hubs when learning the
multi-view embeddings. In this paper, we propose to incorporate the hub
detection task into the multi-view graph embedding framework so that the two
tasks could benefit each other. Specifically, we propose an auto-weighted
framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain
network analysis. The MVGE-HD framework learns a unified graph embedding across
all the views while reducing the potential influence of the hubs on blurring
the boundaries between node clusters in the graph, thus leading to a clear and
discriminative node clustering structure for the graph. We apply MVGE-HD on two
real multi-view brain network datasets (i.e., HIV and Bipolar). The
experimental results demonstrate the superior performance of the proposed
framework in brain network analysis for clinical investigation and application
Semantic memory
The Encyclopedia of Human Behavior, Second Edition is a comprehensive three-volume reference source on human action and reaction, and the thoughts, feelings, and physiological functions behind those actions
Loss of brain inter-frequency hubs in Alzheimer's disease
Alzheimer's disease (AD) causes alterations of brain network structure and
function. The latter consists of connectivity changes between oscillatory
processes at different frequency channels. We proposed a multi-layer network
approach to analyze multiple-frequency brain networks inferred from
magnetoencephalographic recordings during resting-states in AD subjects and
age-matched controls. Main results showed that brain networks tend to
facilitate information propagation across different frequencies, as measured by
the multi-participation coefficient (MPC). However, regional connectivity in AD
subjects was abnormally distributed across frequency bands as compared to
controls, causing significant decreases of MPC. This effect was mainly
localized in association areas and in the cingulate cortex, which acted, in the
healthy group, as a true inter-frequency hub. MPC values significantly
correlated with memory impairment of AD subjects, as measured by the total
recall score. Most predictive regions belonged to components of the
default-mode network that are typically affected by atrophy, metabolism
disruption and amyloid-beta deposition. We evaluated the diagnostic power of
the MPC and we showed that it led to increased classification accuracy (78.39%)
and sensitivity (91.11%). These findings shed new light on the brain functional
alterations underlying AD and provide analytical tools for identifying
multi-frequency neural mechanisms of brain diseases.Comment: 27 pages, 6 figures, 3 tables, 3 supplementary figure
Hierarchical Multi-resolution Mesh Networks for Brain Decoding
We propose a new framework, called Hierarchical Multi-resolution Mesh
Networks (HMMNs), which establishes a set of brain networks at multiple time
resolutions of fMRI signal to represent the underlying cognitive process. The
suggested framework, first, decomposes the fMRI signal into various frequency
subbands using wavelet transforms. Then, a brain network, called mesh network,
is formed at each subband by ensembling a set of local meshes. The locality
around each anatomic region is defined with respect to a neighborhood system
based on functional connectivity. The arc weights of a mesh are estimated by
ridge regression formed among the average region time series. In the final
step, the adjacency matrices of mesh networks obtained at different subbands
are ensembled for brain decoding under a hierarchical learning architecture,
called, fuzzy stacked generalization (FSG). Our results on Human Connectome
Project task-fMRI dataset reflect that the suggested HMMN model can
successfully discriminate tasks by extracting complementary information
obtained from mesh arc weights of multiple subbands. We study the topological
properties of the mesh networks at different resolutions using the network
measures, namely, node degree, node strength, betweenness centrality and global
efficiency; and investigate the connectivity of anatomic regions, during a
cognitive task. We observe significant variations among the network topologies
obtained for different subbands. We, also, analyze the diversity properties of
classifier ensemble, trained by the mesh networks in multiple subbands and
observe that the classifiers in the ensemble collaborate with each other to
fuse the complementary information freed at each subband. We conclude that the
fMRI data, recorded during a cognitive task, embed diverse information across
the anatomic regions at each resolution.Comment: 18 page
Disconnection of network hubs and cognitive impairment after traumatic brain injury.
Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury
Brain enhancement through cognitive training: A new insight from brain connectome
Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive function
Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis
Many species of birds, including pigeons, possess demonstrable cognitive capacities, and some are capable of cognitive feats matching those of apes. Since mammalian cortex is laminar while the avian telencephalon is nucleated, it is natural to ask whether the brains of these two cognitively capable taxa, despite their apparent anatomical dissimilarities, might exhibit common principles of organisation on some level. Complementing recent investigations of macro-scale brain connectivity in mammals, including humans and macaques, we here present the first large-scale wiring diagram for the forebrain of a bird. Using graph theory, we show that the pigeon telencephalon is organised along similar lines to that of a mammal. Both are modular, small-world networks with a connective core of hub nodes that includes prefrontal-like and hippocampal structures. These hub nodes are, topologically speaking, the most central regions of the pigeon's brain, as well as being the most richly connected, implying a crucial role in information flow. Overall, our analysis suggests that indeed, despite the absence of cortical layers and close to 300 million years of separate evolution, the connectivity of the avian brain conforms to the same organisational principles as the mammalian brain
Who is that? Brain networks and mechanisms for identifying individuals
Social animals can identify conspecifics by many forms of sensory input. However, whether the neuronal computations that support this ability to identify individuals rely on modality-independent convergence or involve ongoing synergistic interactions along the multiple sensory streams remains controversial. Direct neuronal measurements at relevant brain sites could address such questions, but this requires better bridging the work in humans and animal models. Here, we overview recent studies in nonhuman primates on voice and face identity-sensitive pathways and evaluate the correspondences to relevant findings in humans. This synthesis provides insights into converging sensory streams in the primate anterior temporal lobe (ATL) for identity processing. Furthermore, we advance a model and suggest how alternative neuronal mechanisms could be tested
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