Highlights
625 views | +0 today
Follow
Your new post is loading...
Your new post is loading...
Scooped by FDVF
Scoop.it!

Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of Calcium Imaging Data

Hierarchy of Neural Organization in the Embryonic Spinal Cord: Granger-Causality Graph Analysis of Calcium Imaging Data | Highlights | Scoop.it

The recent development of genetically encoded calcium indicators enables monitoring in vivo the activity of neuronal populations. Most analysis of these calcium transients relies on linear regression analysis based on the sensory stimulus applied or the behavior observed. To estimate the basic properties of the functional neural circuitry, we propose a networkbased approach based on calcium imaging recorded at single cell resolution. Differently from previous analysis based on cross-correlation, we used Grangercausality estimates to infer activity propagation between the activities of different neurons. The resulting functional networks were then modeled as directed graphs and characterized in terms of connectivity and node centralities. We applied our approach to calcium transients recorded at low frequency (4 Hz) in ventral neurons of the zebrafish spinal cord at the embryonic stage when spontaneous coiling of the tail occurs. Our analysis on population calcium imaging data revealed a strong ipsilateral connectivity and a characteristic hierarchical organization of the network hubs that supported established propagation of activity from rostral to caudal spinal cord. Our method could be used for detecting functional defects in neuronal circuitry during development and pathological conditions.

more...
No comment yet.
Scooped by FDVF
Scoop.it!

Brain Network Analysis From High-Resolution EEG Recordings by the Application of Theoretical Graph Indexes

Brain Network Analysis From High-Resolution EEG Recordings by the Application of Theoretical Graph Indexes | Highlights | Scoop.it

The extraction of the salient characteristics from brain connectivity patterns is an open challenging topic since often the estimated cerebral networks have a relative large size and complex structure. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach would extract significant information from the functional brain networks estimated through different neuroimaging techniques. The present work intends to support the development of the ldquobrain network analysis:rdquo a mathematical tool consisting in a body of indexes based on the graph theory able to improve the comprehension of the complex interactions within the brain. In the present work, we applied for demonstrative purpose some graph indexes to the time-varying networks estimated from a set of high-resolution EEG data in a group of healthy subjects during the performance of a motor task. The comparison with a random benchmark allowed extracting the significant properties of the estimated networks in the representative Alpha (7-12 Hz) band. Altogether, our findings aim at proving how the brain network analysis could reveal important information about the time-frequency dynamics of the functional cortical networks.

more...
No comment yet.
Scooped by FDVF
Scoop.it!

Human brain distinctiveness based on EEG spectral coherence connectivity

Human brain distinctiveness based on EEG spectral coherence connectivity | Highlights | Scoop.it

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence- based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performances show that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that functional connectivity patterns represent effective features for improving EEG-based biometric systems. 

more...
No comment yet.
Scooped by FDVF
Scoop.it!

Cortical functional connectivity networks in normal and spinal cord injured patients: Evaluation by graph analysis

Cortical functional connectivity networks in normal and spinal cord injured patients: Evaluation by graph analysis | Highlights | Scoop.it

The present work aims at analyzing the structure of cortical connectivity during the attempt to move a paralyzed limb by a group of spinal cord injured (SCI) patients. Connectivity patterns were obtained by means of the Directed Transfer Function applied to the cortical signals estimated from high resolution EEG recordings. Electrical activity were estimated in normals (Healthy) and SCI patients on twelve regions of interest (ROIs) coincident with Brodmann areas. Degree distributions showed the presence of few cortical regions with a lot of outgoing connections in all the cortical networks estimated irrespectively of the frequency band investigated. For both of the groups (SCI and Healthy), bilateral cingulate motor area (CMA) acts as hub transmitting information flows. The efficiency index, allowed to assert the ordered properties of such estimated cortical networks in both populations. The comparison of such estimated networks with those obtained from random networks, elicited significant differences (P < 0.05, Bonferroni-corrected for multiple comparisons). A statistical comparison (ANOVA) between SCI patients and healthy subjects showed a significant difference (P < 0.05) between the local efficiency of their respective networks. For three frequency bands (theta 4–7 Hz, alpha 8–12 Hz, and beta 13–29 Hz) the higher value observed in the spinal cord injured population entails a larger level of internal organization and fault tolerance. This fact suggests a sort of compensative mechanism as local response to the alteration in their MIF areas, which is probably due to the indirect effects of the spinal injury. 

more...
No comment yet.
Scooped by FDVF
Scoop.it!

Graph analysis of functional brain networks: practical issues in translational neuroscience

Graph analysis of functional brain networks: practical issues in translational neuroscience | Highlights | Scoop.it

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

more...
No comment yet.
Scooped by FDVF
Scoop.it!

Defecting or Not Defecting: How to “Read” Human Behavior during Cooperative Games by EEG Measurements

Defecting or Not Defecting: How to “Read” Human Behavior during Cooperative Games by EEG Measurements | Highlights | Scoop.it

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity—i.e., the tendency to form two separate subgraphs—than couples playing cooperative or tit-for-tat strategies. The decision to defect can be “read” in advance by evaluating the changes of connectivity pattern in the hyper-brain network.

more...
No comment yet.
Scooped by FDVF
Scoop.it!

Nonparametric resampling of random walks for spectral network clustering

Nonparametric resampling of random walks for spectral network clustering | Highlights | Scoop.it
Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.
more...
No comment yet.
Scooped by FDVF
Scoop.it!

Multiscale topological properties of functional brain networks during motor imagery after stroke

Multiscale topological properties of functional brain networks during motor imagery after stroke | Highlights | Scoop.it

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 of 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 with increased connectivity 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 improve on our understanding of stroke-induced alterations in functional brain networks.

more...
No comment yet.