1,879 research outputs found
Multiscale Topological Properties Of Functional Brain Networks During Motor Imagery After Stroke
In recent years, network analyses have been used to evaluate brain
reorganization following stroke. However, many studies have often focused on
single topological scales, leading to an incomplete model of how focal brain
lesions affect multiple network properties simultaneously and how changes on
smaller scales influence those on larger scales. In an EEG-based experiment on
the performance of hand motor imagery (MI) in 20 patients with unilateral
stroke, we observed that the anatomic lesion affects the functional brain
network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of
the affected hand (Ahand) elicited a significantly lower smallworldness and
local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the
abnormal reduction in Eloc significantly depended on the increase in
interhemispheric connectivity, which was in turn determined primarily by the
rise in regional connectivity in the parieto-occipital sites of the affected
hemisphere. Further, in contrast to the Uhand MI, in which significantly high
connectivity was observed for the contralateral sensorimotor regions of the
unaffected hemisphere, the regions that increased in connection during the
Ahand MI lay in the frontal and parietal regions of the contralaterally
affected hemisphere. Finally, the overall sensorimotor function of our
patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly
predicted by the connectivity of their affected hemisphere. These results
increase our understanding of stroke-induced alterations in functional brain
networks.Comment: Neuroimage, accepted manuscript (unedited version) available online
19-June-201
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics.
Complex cognitive processes require neuronal activity to be coordinated across multiple scales, ranging from local microcircuits to cortex-wide networks. However, multiscale cortical dynamics are not well understood because few experimental approaches have provided sufficient support for hypotheses involving multiscale interactions. To address these limitations, we used, in experiments involving mice, genetically encoded voltage indicator imaging, which measures cortex-wide electrical activity at high spatiotemporal resolution. Here we show that, as mice recovered from anesthesia, scale-invariant spatiotemporal patterns of neuronal activity gradually emerge. We show for the first time that this scale-invariant activity spans four orders of magnitude in awake mice. In contrast, we found that the cortical dynamics of anesthetized mice were not scale invariant. Our results bridge empirical evidence from disparate scales and support theoretical predictions that the awake cortex operates in a dynamical regime known as criticality. The criticality hypothesis predicts that small-scale cortical dynamics are governed by the same principles as those governing larger-scale dynamics. Importantly, these scale-invariant principles also optimize certain aspects of information processing. Our results suggest that during the emergence from anesthesia, criticality arises as information processing demands increase. We expect that, as measurement tools advance toward larger scales and greater resolution, the multiscale framework offered by criticality will continue to provide quantitative predictions and insight on how neurons, microcircuits, and large-scale networks are dynamically coordinated in the brain
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