73,575 research outputs found
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the
semantic and clinical efficacy of automatic segmentation methods relevant for
monitoring osteoarthritis progression.
Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at
two timepoints with ground-truth articular (femoral, tibial, patellar)
cartilage and meniscus segmentations was standardized. Challenge submissions
and a majority-vote ensemble were evaluated using Dice score, average symmetric
surface distance, volumetric overlap error, and coefficient of variation on a
hold-out test set. Similarities in network segmentations were evaluated using
pairwise Dice correlations. Articular cartilage thickness was computed per-scan
and longitudinally. Correlation between thickness error and segmentation
metrics was measured using Pearson's coefficient. Two empirical upper bounds
for ensemble performance were computed using combinations of model outputs that
consolidated true positives and true negatives.
Results: Six teams (T1-T6) submitted entries for the challenge. No
significant differences were observed across all segmentation metrics for all
tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice
correlations between network pairs were high (>0.85). Per-scan thickness errors
were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal
bias (<0.03mm). Low correlations (<0.41) were observed between segmentation
metrics and thickness error. The majority-vote ensemble was comparable to top
performing networks (p=1.0). Empirical upper bound performances were similar
for both combinations (p=1.0).
Conclusion: Diverse networks learned to segment the knee similarly where high
segmentation accuracy did not correlate to cartilage thickness accuracy. Voting
ensembles did not outperform individual networks but may help regularize
individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo
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
FMRI resting slow fluctuations correlate with the activity of fast cortico-cortical physiological connections
Recording of slow spontaneous fluctuations at rest using functional magnetic resonance imaging (fMRI) allows distinct long-range cortical networks to be identified. The neuronal basis of connectivity as assessed by resting-state fMRI still needs to be fully clarified, considering that these signals are an indirect measure of neuronal activity, reflecting slow local variations in de-oxyhaemoglobin concentration. Here, we combined fMRI with multifocal transcranial magnetic stimulation (TMS), a technique that allows the investigation of the causal neurophysiological interactions occurring in specific cortico-cortical connections. We investigated whether the physiological properties of parieto-frontal circuits mapped with short-latency multifocal TMS at rest may have some relationship with the resting-state fMRI measures of specific resting-state functional networks (RSNs). Results showed that the activity of fast cortico-cortical physiological interactions occurring in the millisecond range correlated selectively with the coupling of fMRI slow oscillations within the same cortical areas that form part of the dorsal attention network, i.e., the attention system believed to be involved in reorientation of attention. We conclude that resting-state fMRI ongoing slow fluctuations likely reflect the interaction of underlying physiological cortico-cortical connections
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
Recommended from our members
The effect of the GLP-1 analogue Exenatide on functional connectivity within an NTS-based network in women with and without obesity.
ObjectiveThe differential effect of GLP-1 agonist Exenatide on functional connectivity of the nucleus tractus solitaries (NTS), a key region associated with homeostasis, and on appetite-related behaviours was investigated in women with normal weight compared with women with obesity.MethodsFollowing an 8-h fast, 19 female subjects (11 lean, 8 obese) participated in a 2-d double blind crossover study. Subjects underwent functional magnetic resonance imaging at fast and 30-min post subcutaneous injection of 5 μg of Exenatide or placebo. Functional connectivity was examined with the NTS. Drug-induced functional connectivity changes within and between groups and correlations with appetite measures were examined in a region of interest approach focusing on the thalamus and hypothalamus.ResultsWomen with obesity reported less hunger after drug injection. Exenatide administration increased functional connectivity of the left NTS with the left thalamus and hypothalamus in the obese group only and increased the correlation between NTS functional connectivity and hunger scores in all subjects, but more so in the obese.ConclusionsObesity can impact the effects of Exenatide on brain connectivity, specifically in the NTS and is linked to changes in appetite control. This has implications for the use of GLP-1 analogues in therapeutic interventions
Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level
Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels-group-, individual-, and task-specific, utilizing a combination of well-established statistical methods
Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness
Severe traumatic brain injury can lead to disorders of consciousness (DOC)
characterized by deficit in conscious awareness and cognitive impairment
including coma, vegetative state, minimally consciousness, and lock-in
syndrome. Of crucial importance is to find objective markers that can account
for the large-scale disturbances of brain function to help the diagnosis and
prognosis of DOC patients and eventually the prediction of the coma outcome.
Following recent studies suggesting that the functional organization of brain
networks can be altered in comatose patients, this work analyzes brain
functional connectivity (FC) networks obtained from resting-state functional
magnetic resonance imaging (rs-fMRI). Two approaches are used to estimate the
FC: the Partial Correlation (PC) and the Transfer Entropy (TE). Both the PC and
the TE show significant statistical differences between the group of patients
and control subjects; in brief, the inter-hemispheric PC and the
intra-hemispheric TE account for such differences. Overall, these results
suggest two possible rs-fMRI markers useful to design new strategies for the
management and neuropsychological rehabilitation of DOC patients.Comment: 25 pages; 4 figures; 3 tables; 1 supplementary figure; 4
supplementary tables; accepted for publication in Frontiers in
Neuroinformatic
Instrumental and Analytic Methods for Bolometric Polarimetry
We discuss instrumental and analytic methods that have been developed for the
first generation of bolometric cosmic microwave background (CMB) polarimeters.
The design, characterization, and analysis of data obtained using Polarization
Sensitive Bolometers (PSBs) are described in detail. This is followed by a
brief study of the effect of various polarization modulation techniques on the
recovery of sky polarization from scanning polarimeter data. Having been
successfully implemented on the sub-orbital Boomerang experiment, PSBs are
currently operational in two terrestrial CMB polarization experiments (QUaD and
the Robinson Telescope). We investigate two approaches to the analysis of data
from these experiments, using realistic simulations of time ordered data to
illustrate the impact of instrumental effects on the fidelity of the recovered
polarization signal. We find that the analysis of difference time streams takes
full advantage of the high degree of common mode rejection afforded by the PSB
design. In addition to the observational efforts currently underway, this
discussion is directly applicable to the PSBs that constitute the polarized
capability of the Planck HFI instrument.Comment: 23 pages, 11 figures. for submission to A&
- …