scholarly journals Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0133352 ◽  
Author(s):  
Nicolas Guizard ◽  
Vladimir S. Fonov ◽  
Daniel García-Lorenzo ◽  
Kunio Nakamura ◽  
Bérengère Aubert-Broche ◽  
...  
2018 ◽  
Author(s):  
Mustafa S Salman ◽  
Yuhui Du ◽  
Dongdong Lin ◽  
Zening Fu ◽  
Eswar Damaraju ◽  
...  

AbstractBrain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45±8.9%, sensitivity 0.74±0.11, specificity 0.79±0.11) than STR (accuracy 67.45±8.13%, sensitivity 0.65±0.11, specificity 0.71±0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.


Author(s):  
Nicolas Guizard ◽  
Vladimir S. Fonov ◽  
Daniel García-Lorenzo ◽  
Bérengère Aubert-Broche ◽  
Simon F. Eskildsen ◽  
...  

2005 ◽  
Vol 41 ◽  
pp. 15-30 ◽  
Author(s):  
Helen C. Ardley ◽  
Philip A. Robinson

The selectivity of the ubiquitin–26 S proteasome system (UPS) for a particular substrate protein relies on the interaction between a ubiquitin-conjugating enzyme (E2, of which a cell contains relatively few) and a ubiquitin–protein ligase (E3, of which there are possibly hundreds). Post-translational modifications of the protein substrate, such as phosphorylation or hydroxylation, are often required prior to its selection. In this way, the precise spatio-temporal targeting and degradation of a given substrate can be achieved. The E3s are a large, diverse group of proteins, characterized by one of several defining motifs. These include a HECT (homologous to E6-associated protein C-terminus), RING (really interesting new gene) or U-box (a modified RING motif without the full complement of Zn2+-binding ligands) domain. Whereas HECT E3s have a direct role in catalysis during ubiquitination, RING and U-box E3s facilitate protein ubiquitination. These latter two E3 types act as adaptor-like molecules. They bring an E2 and a substrate into sufficiently close proximity to promote the substrate's ubiquitination. Although many RING-type E3s, such as MDM2 (murine double minute clone 2 oncoprotein) and c-Cbl, can apparently act alone, others are found as components of much larger multi-protein complexes, such as the anaphase-promoting complex. Taken together, these multifaceted properties and interactions enable E3s to provide a powerful, and specific, mechanism for protein clearance within all cells of eukaryotic organisms. The importance of E3s is highlighted by the number of normal cellular processes they regulate, and the number of diseases associated with their loss of function or inappropriate targeting.


2019 ◽  
Vol 47 (6) ◽  
pp. 1733-1747 ◽  
Author(s):  
Christina Klausen ◽  
Fabian Kaiser ◽  
Birthe Stüven ◽  
Jan N. Hansen ◽  
Dagmar Wachten

The second messenger 3′,5′-cyclic nucleoside adenosine monophosphate (cAMP) plays a key role in signal transduction across prokaryotes and eukaryotes. Cyclic AMP signaling is compartmentalized into microdomains to fulfil specific functions. To define the function of cAMP within these microdomains, signaling needs to be analyzed with spatio-temporal precision. To this end, optogenetic approaches and genetically encoded fluorescent biosensors are particularly well suited. Synthesis and hydrolysis of cAMP can be directly manipulated by photoactivated adenylyl cyclases (PACs) and light-regulated phosphodiesterases (PDEs), respectively. In addition, many biosensors have been designed to spatially and temporarily resolve cAMP dynamics in the cell. This review provides an overview about optogenetic tools and biosensors to shed light on the subcellular organization of cAMP signaling.


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