7,679 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
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.
Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition
Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
We investigate the relationship of resting-state fMRI functional connectivity
estimated over long periods of time with time-varying functional connectivity
estimated over shorter time intervals. We show that using Pearson's correlation
to estimate functional connectivity implies that the range of fluctuations of
functional connections over short time scales is subject to statistical
constraints imposed by their connectivity strength over longer scales. We
present a method for estimating time-varying functional connectivity that is
designed to mitigate this issue and allows us to identify episodes where
functional connections are unexpectedly strong or weak. We apply this method to
data recorded from participants, and show that the number of
unexpectedly strong/weak connections fluctuates over time, and that these
variations coincide with intermittent periods of high and low modularity in
time-varying functional connectivity. We also find that during periods of
relative quiescence regions associated with default mode network tend to join
communities with attentional, control, and primary sensory systems. In
contrast, during periods where many connections are unexpectedly strong/weak,
default mode regions dissociate and form distinct modules. Finally, we go on to
show that, while all functional connections can at times manifest stronger
(more positively correlated) or weaker (more negatively correlated) than
expected, a small number of connections, mostly within the visual and
somatomotor networks, do so a disproportional number of times. Our statistical
approach allows the detection of functional connections that fluctuate more or
less than expected based on their long-time averages and may be of use in
future studies characterizing the spatio-temporal patterns of time-varying
functional connectivityComment: 47 Pages, 8 Figures, 4 Supplementary Figure
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Recent studies on analyzing dynamic brain connectivity rely on sliding-window
analysis or time-varying coefficient models which are unable to capture both
smooth and abrupt changes simultaneously. Emerging evidence suggests
state-related changes in brain connectivity where dependence structure
alternates between a finite number of latent states or regimes. Another
challenge is inference of full-brain networks with large number of nodes. We
employ a Markov-switching dynamic factor model in which the state-driven
time-varying connectivity regimes of high-dimensional fMRI data are
characterized by lower-dimensional common latent factors, following a
regime-switching process. It enables a reliable, data-adaptive estimation of
change-points of connectivity regimes and the massive dependencies associated
with each regime. We consider the switching VAR to quantity the dynamic
effective connectivity. We propose a three-step estimation procedure: (1)
extracting the factors using principal component analysis (PCA) and (2)
identifying dynamic connectivity states using the factor-based switching vector
autoregressive (VAR) models in a state-space formulation using Kalman filter
and expectation-maximization (EM) algorithm, and (3) constructing the
high-dimensional connectivity metrics for each state based on subspace
estimates. Simulation results show that our proposed estimator outperforms the
K-means clustering of time-windowed coefficients, providing more accurate
estimation of regime dynamics and connectivity metrics in high-dimensional
settings. Applications to analyzing resting-state fMRI data identify dynamic
changes in brain states during rest, and reveal distinct directed connectivity
patterns and modular organization in resting-state networks across different
states.Comment: 21 page
Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study.
Although individuals at clinical high risk (CHR) for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ), it remains unclear whether their resting-state brain intrinsic functional networks (INs) show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs), 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive), and 58 early illness SZ (ESZ) patients (among which 53 patients took antipsychotic medication) within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA) and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern) from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs) from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a common abnormality in CHR and ESZ groups. Some brain regions also showed a CHR-unique alteration (primarily the CHR-increasing connectivity). In summary, CHR individuals generally showed intermediate connectivity between HCs and ESZ patients across multiple INs, suggesting that some dysconnectivity patterns evident in ESZ predate psychosis in attenuated form during the psychosis risk stage. Hence, these connectivity measures may serve as possible biomarkers to predict schizophrenia progression
Brain covariance selection: better individual functional connectivity models using population prior
Spontaneous brain activity, as observed in functional neuroimaging, has been
shown to display reproducible structure that expresses brain architecture and
carries markers of brain pathologies. An important view of modern neuroscience
is that such large-scale structure of coherent activity reflects modularity
properties of brain connectivity graphs. However, to date, there has been no
demonstration that the limited and noisy data available in spontaneous activity
observations could be used to learn full-brain probabilistic models that
generalize to new data. Learning such models entails two main challenges: i)
modeling full brain connectivity is a difficult estimation problem that faces
the curse of dimensionality and ii) variability between subjects, coupled with
the variability of functional signals between experimental runs, makes the use
of multiple datasets challenging. We describe subject-level brain functional
connectivity structure as a multivariate Gaussian process and introduce a new
strategy to estimate it from group data, by imposing a common structure on the
graphical model in the population. We show that individual models learned from
functional Magnetic Resonance Imaging (fMRI) data using this population prior
generalize better to unseen data than models based on alternative
regularization schemes. To our knowledge, this is the first report of a
cross-validated model of spontaneous brain activity. Finally, we use the
estimated graphical model to explore the large-scale characteristics of
functional architecture and show for the first time that known cognitive
networks appear as the integrated communities of functional connectivity graph.Comment: in Advances in Neural Information Processing Systems, Vancouver :
Canada (2010
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