14,172 research outputs found
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Altered rich club and frequency-dependent subnetworks organization in mild traumatic brain injury: A MEG resting-state study
Functional brain connectivity networks exhibit āsmall-worldā characteristics and some
of these networks follow a ārich-clubā organization, whereby a few nodes of high
connectivity (hubs) tend to connect more densely among themselves than to nodes
of lower connectivity. The Current study followed an āattack strategyā to compare the
rich-club and small-world network organization models using Magnetoencephalographic
(MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically
healthy controls to identify the topology that describes the underlying intrinsic brain
network organization. We hypothesized that the reduction in global efficiency caused
by an attack targeting a modelās hubs would reveal the ātrueā underlying topological
organization. Connectivity networks were estimated using mutual information as
the basis for cross-frequency coupling. Our results revealed a prominent rich-club
network organization for both groups. In particular, mTBI patients demonstrated hypersynchronization
among rich-club hubs compared to controls in the d band and the
d-g1, "-g1, and b-g2 frequency pairs. Moreover, rich-club hubs in mTBI patients
were overrepresented in right frontal brain areas, from " to g1 frequencies, and
underrepresented in left occipital regions in the d-b, d-g1, "-b, and b-g2 frequency pairs.
These findings indicate that the rich-club organization of resting-state MEG, considering
its role in information integration and its vulnerability to various disorders like mTBI, may
have a significant predictive value in the development of reliable biomarkers to help the
validation of the recovery frommTBI. Furthermore, the proposed approachmight be used
as a validation tool to assess patient recovery
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Consciousness, cognition, and the hierarchy of context: extending the global neuronal workspace model
We adapt an information theory analysis of interacting cognitive biological and social modules to the problem of the global neuronal workspace, the new standard neuroscience paradigm for consciousness. Tunable punctuation emerges in a natural way, suggesting the possibility of fitting appropriate phase transition power law, and away from transition, generalized Onsager relation expressions, to observational data on conscious reaction. The development can be extended in a straightforward manner to include psychosocial stress, culture, or other cognitive modules which constitute a structured, embedding hierarchy of contextual constraints acting at a slower rate than neuronal function itself. This produces a 'biopsychosociocultural' model of individual consciousness that, while otherwise quite close to the standard treatment, meets compelling philosophical and other objections to brain-only descriptions
Structured Psychosocial Stress and Therapeutic Intervention: Toward a Realistic Biological Medicine
Using generalized 'language of thought' arguments appropriate to interacting cognitive modules, we explore how disease states can interact with medical treatment, including, but not limited to, drug therapy. The feedback between treatment and response creates a kind of idiotypic 'hall of mirrors' generating a pattern of 'efficacy', 'treatment failure', and 'adverse reactions' which will, from a Rate Distortion perspective, embody a distorted image of externally-imposed structured psychosocial stress. This analysis, unlike current pharmacogenetics, does not either reify 'race' or blame the victim by using genetic structure to place the locus-of-control within a group or individual. Rather, it suggests that a comparatively simple series of questions to identify longitudinal and cross-sectional stressors may provide more effective guidance for specification of individual therapy than complicated genotyping strategies of dubious meaning. These latter are likely to be both very expensive and utterly blind to the impact of structured psychosocial stress -- a euphemism for various forms of racism and ethnic cleansing -- which, we contend, is often a principal determinant of treatment outcome at both individual and community levels of organization. We propose, to effectively address 'health disparities' between populations, and in contrast to current biomedical ideology based on a simplistic genetic determinism, a richer program of biological medicine reflecting Lewontin's 'triple helix' of genes, environment, and development, a program more in concert with the realities of a basic human biology marked by hypersociality unusual in vertibrates. Aggressive social, economic, and other policies of affirmative action to redress the persisting burdens of history would be an integral component of any such project
Reconfiguration of dominant coupling modes in mild traumatic brain injury mediated by Ī“-band activity: a resting state MEG study
During the last few years, rich-club (RC) organization has been studied as a possible brain-connectivity organization model for large-scale brain networks. At the same time, empirical and simulated data of neurophysiological models have demonstrated the significant role of intra-frequency and inter-frequency coupling among distinct brain areas. The current study investigates further the importance of these couplings using recordings of resting-state magnetoencephalographic activity obtained from 30 mild traumatic brain injury (mTBI) subjects and 50 healthy controls. Intra-frequency and inter-frequency coupling modes are incorporated in a single graph to detect group differences within individual rich-club subnetworks (type I networks) and networks connecting RC nodes with the rest of the nodes (type II networks). Our results show a higher probability of inter-frequency coupling for (Ī“āĪ³1), (Ī“āĪ³2), (ĪøāĪ²), (ĪøāĪ³2), (Ī±āĪ³2), (Ī³1āĪ³2) and intra-frequency coupling for (Ī³1āĪ³1) and (Ī“āĪ“) for both type I and type II networks in the mTBI group. Additionally, mTBI and control subjects can be correctly classified with high accuracy (98.6%), whereas a general linear regression model can effectively predict the subject group using the ratio of type I and type II coupling in the (Ī“, Īø), (Ī“, Ī²), (Ī“, Ī³1), and (Ī“, Ī³2) frequency pairs. These findings support the presence of an RC organization simultaneously with dominant frequency interactions within a single functional graph. Our results demonstrate a hyperactivation of intrinsic RC networks in mTBI subjects compared to controls, which can be seen as a plausible compensatory mechanism for alternative frequency-dependent routes of information flow in mTBI subjects
Improving the reliability of network metrics in structural brain networks by integrating different network weighting strategies into a single graph
Structural brain networks estimated from diffusion MRI (dMRI) via tractography have been widely studied in healthy controls and in patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS) can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost full-weighted. Here, we scanned 5 healthy participants 5 times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROIs) from the AAL template. The edges were weighted according to nine different methods.We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an integrated weighted structural brain network (ISWBN). Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of : a) intra-class correlation coefficient (ICC) of well-known network metrics, both node-wise and per network level; and b) the recognition accuracy of each subject over the rest of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject; after first applying our proposed topological filtering scheme. Based on a threshold that the network-level ICC should be > 0.90, our findings revealed six out of nine NWS lead to unreliable results at the network-level, while all nine NWS were unreliable at the node-level. In comparison, our proposed ISWBN performed as well as the best-performing individual NWS at the network-level, and the ICC was higher compared to all individual NWS at the node-level. Importantly, both network- and node-wise ICCs of network metrics derived from the topologically filtered ISBWN(ISWBNTF), were further improved compared to non-filtered ISWBN. Finally, in the recognition accuracy tests, we assigned each single ISWBNTF to the correct subject. Overall, these findings suggest that the proposed methodology results in improved characterisation of genuine between-subject differences in connectivit
Why are probabilistic laws governing quantum mechanics and neurobiology?
We address the question: Why are dynamical laws governing in quantum
mechanics and in neuroscience of probabilistic nature instead of being
deterministic? We discuss some ideas showing that the probabilistic option
offers advantages over the deterministic one.Comment: 40 pages, 8 fig
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