31,984 research outputs found
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
A great improvement to the insight on brain function that we can get from
fMRI data can come from effective connectivity analysis, in which the flow of
information between even remote brain regions is inferred by the parameters of
a predictive dynamical model. As opposed to biologically inspired models, some
techniques as Granger causality (GC) are purely data-driven and rely on
statistical prediction and temporal precedence. While powerful and widely
applicable, this approach could suffer from two main limitations when applied
to BOLD fMRI data: confounding effect of hemodynamic response function (HRF)
and conditioning to a large number of variables in presence of short time
series. For task-related fMRI, neural population dynamics can be captured by
modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI
on the other hand, the absence of explicit inputs makes this task more
difficult, unless relying on some specific prior physiological hypothesis. In
order to overcome these issues and to allow a more general approach, here we
present a simple and novel blind-deconvolution technique for BOLD-fMRI signal.
Coming to the second limitation, a fully multivariate conditioning with short
and noisy data leads to computational problems due to overfitting. Furthermore,
conceptual issues arise in presence of redundancy. We thus apply partial
conditioning to a limited subset of variables in the framework of information
theory, as recently proposed. Mixing these two improvements we compare the
differences between BOLD and deconvolved BOLD level effective networks and draw
some conclusions
Complex networks in brain electrical activity
We analyze the complex networks associated with brain electrical activity.
Multichannel EEG measurements are first processed to obtain 3D voxel
activations using the tomographic algorithm LORETA. Then, the correlation of
the current intensity activation between voxel pairs is computed to produce a
voxel cross-correlation coefficient matrix. Using several correlation
thresholds, the cross-correlation matrix is then transformed into a network
connectivity matrix and analyzed. To study a specific example, we selected data
from an earlier experiment focusing on the MMN brain wave. The resulting
analysis highlights significant differences between the spatial activations
associated with Standard and Deviant tones, with interesting physiological
implications. When compared to random data networks, physiological networks are
more connected, with longer links and shorter path lengths. Furthermore, as
compared to the Deviant case, Standard data networks are more connected, with
longer links and shorter path lengths--i.e., with a stronger ``small worlds''
character. The comparison between both networks shows that areas known to be
activated in the MMN wave are connected. In particular, the analysis supports
the idea that supra-temporal and inferior frontal data work together in the
processing of the differences between sounds by highlighting an increased
connectivity in the response to a novel sound.Comment: 22 pages, 5 figures. Starlab preprint. This version is an attempt to
include better figures (no content change
Functional network changes and cognitive control in schizophrenia
Cognitive control is a cognitive and neural mechanism that contributes to managing the complex demands of day-to-day life. Studies have suggested that functional impairments in cognitive control associated brain circuitry contribute to a broad range of higher cognitive deficits in schizophrenia. To examine this issue, we assessed functional connectivity networks in healthy adults and individuals with schizophrenia performing tasks from two distinct cognitive domains that varied in demands for cognitive control, the RiSE episodic memory task and DPX goal maintenance task. We characterized general and cognitive control-specific effects of schizophrenia on functional connectivity within an expanded frontal parietal network (FPN) and quantified network topology properties using graph analysis. Using the network based statistic (NBS), we observed greater network functional connectivity in cognitive control demanding conditions during both tasks in both groups in the FPN, and demonstrated cognitive control FPN specificity against a task independent auditory network. NBS analyses also revealed widespread connectivity deficits in schizophrenia patients across all tasks. Furthermore, quantitative changes in network topology associated with diagnostic status and task demand were observed. The present findings, in an analysis that was limited to correct trials only, ensuring that subjects are on task, provide critical insights into network connections crucial for cognitive control and the manner in which brain networks reorganize to support such control. Impairments in this mechanism are present in schizophrenia and these results highlight how cognitive control deficits contribute to the pathophysiology of this illness
Network Physiology reveals relations between network topology and physiological function
The human organism is an integrated network where complex physiologic
systems, each with its own regulatory mechanisms, continuously interact, and
where failure of one system can trigger a breakdown of the entire network.
Identifying and quantifying dynamical networks of diverse systems with
different types of interactions is a challenge. Here, we develop a framework to
probe interactions among diverse systems, and we identify a physiologic
network. We find that each physiologic state is characterized by a specific
network structure, demonstrating a robust interplay between network topology
and function. Across physiologic states the network undergoes topological
transitions associated with fast reorganization of physiologic interactions on
time scales of a few minutes, indicating high network flexibility in response
to perturbations. The proposed system-wide integrative approach may facilitate
the development of a new field, Network Physiology.Comment: 12 pages, 9 figure
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