180 research outputs found
A group model for stable multi-subject ICA on fMRI datasets
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 sets of mutually correlated brain
regions without prior information on the time course of these regions. Some of
these sets of regions, interpreted as functional networks, have recently been
used to provide markers of brain diseases and open the road to paradigm-free
population comparisons. Such group studies raise the question of modeling
subject variability within ICA: how can the patterns representative of a group
be modeled and estimated via ICA for reliable inter-group comparisons? In this
paper, we propose a hierarchical model for patterns in multi-subject fMRI
datasets, akin to mixed-effect group models used in linear-model-based
analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based
on i) probabilistic dimension reduction of the individual data, ii) canonical
correlation analysis to identify a data subspace common to the group iii)
ICA-based pattern extraction. In addition, we introduce a procedure based on
cross-validation to quantify the stability of ICA patterns at the level of the
group. We compare our method with state-of-the-art multi-subject fMRI ICA
methods and show that the features extracted using our procedure are more
reproducible at the group level on two datasets of 12 healthy controls: a
resting-state and a functional localizer study
Experimental investigations of critical hydraulic gradients for a soil prone to suffusion
The presence of soils, which are at the limit state of internal stability, is a potential risk to earthworks under seepage flow. Therefore, it is necessary to identify unstable soils and to estimate hydraulic gradients at which the suffusion can be initiated respectively progressed. An experimental study has been carried out to quantify critical hydraulic gradients for a widely graded soil. For the tested soil, in downwards vertical percolation experiments, the global critical hydraulic gradients lie in the different ranges between icrit = 0:1 to 5:5 with dependency on the particle arrangement. The critical hydraulic gradient was investigated using various types of sample preparation technique. Moreover, suffusion tests using several types of samples with the same particle size distribution have been carried out. It states that for such a widely graded soil, the main problem is the particle arrangement. In other words, the suffusion might be not problematic if there is no segregation. Sometimes a specific amount of segregation also stabilizes the sample against suffusion. The common way of sample preparation delivers comparable results to the results of other researchers
Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?
Correlations in the signal observed via functional Magnetic Resonance Imaging
(fMRI), are expected to reveal the interactions in the underlying neural
populations through hemodynamic response. In particular, they highlight
distributed set of mutually correlated regions that correspond to brain
networks related to different cognitive functions. Yet graph-theoretical
studies of neural connections give a different picture: that of a highly
integrated system with small-world properties: local clustering but with short
pathways across the complete structure. We examine the conditional independence
properties of the fMRI signal, i.e. its Markov structure, to find realistic
assumptions on the connectivity structure that are required to explain the
observed functional connectivity. In particular we seek a decomposition of the
Markov structure into segregated functional networks using decomposable graphs:
a set of strongly-connected and partially overlapping cliques. We introduce a
new method to efficiently extract such cliques on a large, strongly-connected
graph. We compare methods learning different graph structures from functional
connectivity by testing the goodness of fit of the model they learn on new
data. We find that summarizing the structure as strongly-connected networks can
give a good description only for very large and overlapping networks. These
results highlight that Markov models are good tools to identify the structure
of brain connectivity from fMRI signals, but for this purpose they must reflect
the small-world properties of the underlying neural systems
Identification of Early Intermediates of Caspase Activation Using Selective Inhibitors and Activity-Based Probes
Caspases are cysteine proteases that are key effectors in apoptotic cell death. Currently, there is a lack of tools that can be used to monitor the regulation of specific caspases in the context of distinct apoptotic programs. We describe the development of highly selective inhibitors and active site probes and their applications to directly monitor executioner (caspase-3 and -7) and initiator (caspase-8 and -9) caspase activity. Specifically, these reagents were used to dissect the kinetics of caspase activation upon stimulation of apoptosis in cell-free extracts and intact cells. These studies identified a full-length caspase-7 intermediate that becomes catalytically activated early in the pathway and whose further processing is mediated by mature executioner caspases rather than initiator caspases. This form also shows distinct inhibitor sensitivity compared to processed caspase-7. Our data suggest that caspase-7 activation proceeds through a previously uncharacterized intermediate that is formed without cleavage of the intact zymogen
Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools
Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis
Neural primacy of the salience processing system in schizophrenia
For effective information processing, two large-scale distributed neural networks appear to be critical: a multimodal executive system anchored on the dorsolateral prefrontal cortex (DLPFC) and a salience system anchored on the anterior insula. Aberrant interaction among distributed networks is a feature of psychiatric disorders such as schizophrenia. We used whole-brain Granger causal modeling using resting fMRI and observed a significant failure of both the feedforward and reciprocal influence between the insula and the DLPFC in schizophrenia. Further, a significant failure of directed influence from bilateral visual cortices to the insula was also seen in patients. These findings provide compelling evidence for a breakdown of the salience-execution loop in the clinical expression of psychosis. In addition, this offers a parsimonious explanation for the often-observed “frontal inefficiency,” the failure to recruit prefrontal system when salient or novel information becomes available in patients with schizophrenia
Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases
Postmortem MRI allows brain anatomy to be examined at high resolution and to
link pathology measures with morphometric measurements. However, automated
segmentation methods for brain mapping in postmortem MRI are not well
developed, primarily due to limited availability of labeled datasets, and
heterogeneity in scanner hardware and acquisition protocols. In this work, we
present a high resolution of 135 postmortem human brain tissue specimens imaged
at 0.3 mm isotropic using a T2w sequence on a 7T whole-body MRI scanner.
We developed a deep learning pipeline to segment the cortical mantle by
benchmarking the performance of nine deep neural architectures, followed by
post-hoc topological correction. We then segment four subcortical structures
(caudate, putamen, globus pallidus, and thalamus), white matter
hyperintensities, and the normal appearing white matter. We show generalizing
capabilities across whole brain hemispheres in different specimens, and also on
unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence
at 7T. We then compute localized cortical thickness and volumetric measurements
across key regions, and link them with semi-quantitative neuropathological
ratings. Our code, Jupyter notebooks, and the containerized executables are
publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website:
https://pulkit-khandelwal.github.io/exvivo-brain-upen
The Distressed Brain: A Group Blind Source Separation Analysis on Tinnitus
Background: Tinnitus, the perception of a sound without an external sound source, can lead to variable amounts of distress. Methodology: In a group of tinnitus patients with variable amounts of tinnitus related distress, as measured by the Tinnitus Questionnaire (TQ), an electroencephalography (EEG) is performed, evaluating the patients ’ resting state electrical brain activity. This resting state electrical activity is compared with a control group and between patients with low (N = 30) and high distress (N = 25). The groups are homogeneous for tinnitus type, tinnitus duration or tinnitus laterality. A group blind source separation (BSS) analysis is performed using a large normative sample (N = 84), generating seven normative components to which high and low tinnitus patients are compared. A correlation analysis of the obtained normative components ’ relative power and distress is performed. Furthermore, the functional connectivity as reflected by lagged phase synchronization is analyzed between the brain areas defined by the components. Finally, a group BSS analysis on the Tinnitus group as a whole is performed. Conclusions: Tinnitus can be characterized by at least four BSS components, two of which are posterior cingulate based, one based on the subgenual anterior cingulate and one based on the parahippocampus. Only the subgenual component correlates with distress. When performed on a normative sample, group BSS reveals that distress is characterized by two anterior cingulate based components. Spectral analysis of these components demonstrates that distress in tinnitus is relate
Chemical genetics strategies for identification of molecular targets
Chemical genetics is an emerging field that can be used to study the interactions of chemical compounds, including natural products, with proteins. Usually, the identification of molecular targets is the starting point for studying a drug’s mechanism of action and this has been a crucial step in understanding many biological processes. While a great variety of target identification methods have been developed over the last several years, there are still many bioactive compounds whose target proteins have not yet been revealed because no routine protocols can be adopted. This review contains information concerning the most relevant principles of chemical genetics with special emphasis on the different genomic and proteomic approaches used in forward chemical genetics to identify the molecular targets of the bioactive compounds, the advantages and disadvantages of each and a detailed list of successful examples
of molecular targets identified with these approaches
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