1,686 research outputs found
Learning and comparing functional connectomes across subjects
Functional connectomes capture brain interactions via synchronized
fluctuations in the functional magnetic resonance imaging signal. If measured
during rest, they map the intrinsic functional architecture of the brain. With
task-driven experiments they represent integration mechanisms between
specialized brain areas. Analyzing their variability across subjects and
conditions can reveal markers of brain pathologies and mechanisms underlying
cognition. Methods of estimating functional connectomes from the imaging signal
have undergone rapid developments and the literature is full of diverse
strategies for comparing them. This review aims to clarify links across
functional-connectivity methods as well as to expose different steps to perform
a group study of functional connectomes
Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations
For more than a century, cerebral cartography has been driven by
investigations of structural and morphological properties of the brain across
spatial scales and the temporal/functional phenomena that emerge from these
underlying features. The next era of brain mapping will be driven by studies
that consider both of these components of brain organization simultaneously --
elucidating their interactions and dependencies. Using this guiding principle,
we explored the origin of slowly fluctuating patterns of synchronization within
the topological core of brain regions known as the rich club, implicated in the
regulation of mood and introspection. We find that a constellation of densely
interconnected regions that constitute the rich club (including the anterior
insula, amygdala, and precuneus) play a central role in promoting a stable,
dynamical core of spontaneous activity in the primate cortex. The slow time
scales are well matched to the regulation of internal visceral states,
corresponding to the somatic correlates of mood and anxiety. In contrast, the
topology of the surrounding "feeder" cortical regions show unstable, rapidly
fluctuating dynamics likely crucial for fast perceptual processes. We discuss
these findings in relation to psychiatric disorders and the future of
connectomics.Comment: 35 pages, 6 figure
Task-related edge density (TED) - a new method for revealing large-scale network formation in fMRI data of the human brain
The formation of transient networks in response to external stimuli or as a
reflection of internal cognitive processes is a hallmark of human brain
function. However, its identification in fMRI data of the human brain is
notoriously difficult. Here we propose a new method of fMRI data analysis that
tackles this problem by considering large-scale, task-related synchronisation
networks. Networks consist of nodes and edges connecting them, where nodes
correspond to voxels in fMRI data, and the weight of an edge is determined via
task-related changes in dynamic synchronisation between their respective times
series. Based on these definitions, we developed a new data analysis algorithm
that identifies edges in a brain network that differentially respond in unison
to a task onset and that occur in dense packs with similar characteristics.
Hence, we call this approach "Task-related Edge Density" (TED). TED proved to
be a very strong marker for dynamic network formation that easily lends itself
to statistical analysis using large scale statistical inference. A major
advantage of TED compared to other methods is that it does not depend on any
specific hemodynamic response model, and it also does not require a
presegmentation of the data for dimensionality reduction as it can handle large
networks consisting of tens of thousands of voxels. We applied TED to fMRI data
of a fingertapping task provided by the Human Connectome Project. TED revealed
network-based involvement of a large number of brain areas that evaded
detection using traditional GLM-based analysis. We show that our proposed
method provides an entirely new window into the immense complexity of human
brain function.Comment: 21 pages, 11 figure
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
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