383 research outputs found
CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract meaningful patterns without prior
information. However, ICA is not robust to mild data variation and remains a
parameter-sensitive algorithm. The validity of the extracted patterns is hard
to establish, as well as the significance of differences between patterns
extracted from different groups of subjects. We start from a generative model
of the fMRI group data to introduce a probabilistic ICA pattern-extraction
algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and
canonical correlation analysis, our method is auto-calibrated and identifies
the group-reproducible data subspace before performing ICA. We compare our
method to state-of-the-art multi-subject fMRI ICA methods and show that the
features extracted are more reproducible
Differential electrophysiological response during rest, self-referential, and non-self-referential tasks in human posteromedial cortex
The electrophysiological basis for higher brain activity during rest and internally directed cognition within the human default mode network
(DMN) remains largely unknown. Here we use intracranial recordings in
the human posteromedial cortex (PMC), a core node within the DMN,
during conditions of cued rest, autobiographical judgments, and
arithmetic processing. We found a heterogeneous profile of PMC
responses in functional, spatial, and temporal domains. Although the
majority of PMC sites showed increased broad gamma band activity
(30-180 Hz) during rest, some PMC sites, proximal to the retrosplenial
cortex, responded selectively to autobiographical stimuli. However, no
site responded to both conditions, even though they were located within
the boundaries of the DMN identified with resting-state functional
imaging and similarly deactivated during arithmetic processing. These
findings, which provide electrophysiological evidence for heterogeneity
within the core of the DMN, will have important implications for
neuroimaging studies of the DMN
The resting human brain and motor learning.
Functionally related brain networks are engaged even in the absence of an overt behavior. The role of this resting state activity, evident as low-frequency fluctuations of BOLD (see [1] for review, [2-4]) or electrical [5, 6] signals, is unclear. Two major proposals are that resting state activity supports introspective thought or supports responses to future events [7]. An alternative perspective is that the resting brain actively and selectively processes previous experiences [8]. Here we show that motor learning can modulate subsequent activity within resting networks. BOLD signal was recorded during rest periods before and after an 11 min visuomotor training session. Motor learning but not motor performance modulated a fronto-parietal resting state network (RSN). Along with the fronto-parietal network, a cerebellar network not previously reported as an RSN was also specifically altered by learning. Both of these networks are engaged during learning of similar visuomotor tasks [9-22]. Thus, we provide the first description of the modulation of specific RSNs by prior learning--but not by prior performance--revealing a novel connection between the neuroplastic mechanisms of learning and resting state activity. Our approach may provide a powerful tool for exploration of the systems involved in memory consolidation
Scalable Group Level Probabilistic Sparse Factor Analysis
Many data-driven approaches exist to extract neural representations of
functional magnetic resonance imaging (fMRI) data, but most of them lack a
proper probabilistic formulation. We propose a group level scalable
probabilistic sparse factor analysis (psFA) allowing spatially sparse maps,
component pruning using automatic relevance determination (ARD) and subject
specific heteroscedastic spatial noise modeling. For task-based and resting
state fMRI, we show that the sparsity constraint gives rise to components
similar to those obtained by group independent component analysis. The noise
modeling shows that noise is reduced in areas typically associated with
activation by the experimental design. The psFA model identifies sparse
components and the probabilistic setting provides a natural way to handle
parameter uncertainties. The variational Bayesian framework easily extends to
more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1
Abnormal resting-state functional connectivity in progressive supranuclear palsy and corticobasal syndrome
Background: Pathological and MRI-based evidence suggests that multiple brain structures
are likely to be involved in functional disconnection between brain areas. Few studies
have investigated resting-state functional connectivity (rsFC) in progressive supranuclear
palsy (PSP) and corticobasal syndrome (CBS). In this study, we investigated within- and
between-network rsFC abnormalities in these two conditions.
Methods: Twenty patients with PSP, 11 patients with CBS, and 16 healthy subjects (HS)
underwent a resting-state fMRI study. Resting-state networks (RSNs) were extracted to
evaluate within- and between-network rsFC using the Melodic and FSLNets software
packages.
results: Increased within-network rsFC was observed in both PSP and CBS patients,
with a larger number of RSNs being involved in CBS. Within-network cerebellar rsFC
positively correlated with mini-mental state examination scores in patients with PSP.
Compared to healthy volunteers, PSP and CBS patients exhibit reduced functional
connectivity between the lateral visual and auditory RSNs, with PSP patients additionally
showing lower functional connectivity between the cerebellar and insular RSNs.
Moreover, rsFC between the salience and executive-control RSNs was increased in
patients with CBS compared to HS.
conclusion: This study provides evidence of functional brain reorganization in both PSP
and CBS. Increased within-network rsFC could represent a higher degree of synchronization
in damaged brain areas, while between-network rsFC abnormalities may mainly
reflect degeneration of long-range white matter fibers
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
Model order selection criteria: comparative study and applications
A practical application of information theoretic criteria is presented in this paper. Eigenvalue decomposition of the signal correlation matrixbased AIC, MDL and MIBS criteria are investigated and used for online estimation of time varying parameters of harmonic signals in power systems.===PL===Artykuł przedstawia kryteria i porównanie metod redukcji modelu procesu. Przedstawiono i porównano różne kryteria bazujące na dekompozycji macierzy korelacji według wartości własnych: AIC, MDL i MIBS. Porównania dokonano na sygnałach harmonicznych odpowiadających układowi niestacjonarnemu
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