661,584 research outputs found
Interview with the Coordinator Prof. Giuseppe Di Giovanni, University of Malta, Department of Physiology and Biochemistry
Interview with the Coordinator of the Malta Neuroscience Network Programme, Prof. Giuseppe Di Giovanni regarding the Malta Neuroscience Net-
work. "With the creation of the Malta Neuroscience Network, we will be keeping up with the most important developments with regard to brain research world-
wide: multi-disciplinary collaboration. Understanding
the way the brain works, and above all brain diseases is
extremely complicated, and requires the involvement of
researchers coming from a number of diff erent scientifi c
disciplines and clinical areas collaborating in new ways."peer-reviewe
Brain networks under attack : robustness properties and the impact of lesions
A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research
Exponential Random Graph Modeling for Complex Brain Networks
Exponential random graph models (ERGMs), also known as p* models, have been
utilized extensively in the social science literature to study complex networks
and how their global structure depends on underlying structural components.
However, the literature on their use in biological networks (especially brain
networks) has remained sparse. Descriptive models based on a specific feature
of the graph (clustering coefficient, degree distribution, etc.) have dominated
connectivity research in neuroscience. Corresponding generative models have
been developed to reproduce one of these features. However, the complexity
inherent in whole-brain network data necessitates the development and use of
tools that allow the systematic exploration of several features simultaneously
and how they interact to form the global network architecture. ERGMs provide a
statistically principled approach to the assessment of how a set of interacting
local brain network features gives rise to the global structure. We illustrate
the utility of ERGMs for modeling, analyzing, and simulating complex
whole-brain networks with network data from normal subjects. We also provide a
foundation for the selection of important local features through the
implementation and assessment of three selection approaches: a traditional
p-value based backward selection approach, an information criterion approach
(AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF
approach serves as the best method given the scientific interest in being able
to capture and reproduce the structure of fitted brain networks
Alterations in functional brain network structure induced by subchronic phencyclidine (PCP) treatment parallel those seen in schizophrenia
Abstract of poster presentation shown at the 2nd Biennial Schizophrenia International Research Conference on Alterations in functional brain network structure induced by subchronic phencyclidine (PCP) treatment parallel those seen in schizophrenia
Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance
In cognitive network neuroscience, the connectivity and community structure
of the brain network is related to cognition. Much of this research has focused
on two measures of connectivity - modularity and flexibility - which frequently
have been examined in isolation. By using resting state fMRI data from 52 young
adults, we investigate the relationship between modularity, flexibility and
performance on cognitive tasks. We show that flexibility and modularity are
highly negatively correlated. However, we also demonstrate that flexibility and
modularity make unique contributions to explain task performance, with
modularity predicting performance for simple tasks and flexibility predicting
performance on complex tasks that require cognitive control and executive
functioning. The theory and results presented here allow for stronger links
between measures of brain network connectivity and cognitive processes.Comment: 37 pages; 7 figure
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