28,080 research outputs found
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
Sparse Predictive Structure of Deconvolved Functional Brain Networks
The functional and structural representation of the brain as a complex
network is marked by the fact that the comparison of noisy and intrinsically
correlated high-dimensional structures between experimental conditions or
groups shuns typical mass univariate methods. Furthermore most network
estimation methods cannot distinguish between real and spurious correlation
arising from the convolution due to nodes' interaction, which thus introduces
additional noise in the data. We propose a machine learning pipeline aimed at
identifying multivariate differences between brain networks associated to
different experimental conditions. The pipeline (1) leverages the deconvolved
individual contribution of each edge and (2) maps the task into a sparse
classification problem in order to construct the associated "sparse deconvolved
predictive network", i.e., a graph with the same nodes of those compared but
whose edge weights are defined by their relevance for out of sample predictions
in classification. We present an application of the proposed method by decoding
the covert attention direction (left or right) based on the single-trial
functional connectivity matrix extracted from high-frequency
magnetoencephalography (MEG) data. Our results demonstrate how network
deconvolution matched with sparse classification methods outperforms typical
approaches for MEG decoding
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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