Topographic electrophysiological signatures of FMRI Resting State Networks

Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. Methodology/Principal Findings: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Conclusions/Significance: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.

Saliva/pathogen biomarker signatures and periodontal disease progression

The purpose of this study was to determine the role of saliva-derived biomarkers and periodontal pathogens during periodontal disease progression (PDP). One hundred human participants were recruited into a 12-month investigation. They were seen bi-monthly for saliva and clinical measures and bi-annually for subtraction radiography, serum and plaque biofilm assessments. Saliva and serum were analyzed with protein arrays for 14 pro-inflammatory and bone turnover markers, while qPCR was used for detection of biofilm. A hierarchical clustering algorithm was used to group study participants based on clinical, microbiological, salivary/serum biomarkers, and PDP. Eighty-three individuals completed the six-month monitoring phase, with 39 [corrected] exhibiting PDP, while 44 [corrected] demonstrated stability. Participants assembled into three clusters based on periodontal pathogens, serum and salivary biomarkers. Cluster 1 members displayed high salivary biomarkers and biofilm; 71% [corrected] of these individuals were undergoing PDP. Cluster 2 members displayed low biofilm and biomarker levels; 76% [corrected] of these individuals were stable. Cluster 3 members were not discriminated by PDP status; however, cluster stratification followed groups 1 and 2 based on thresholds of salivary biomarkers and biofilm pathogens. The association of cluster membership to PDP was highly significant (p < 0.0007). [corrected] The use of salivary and biofilm biomarkers offers potential for the identification of PDP or stability (ClinicalTrials.gov number, CT00277745).

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Hormonally defined pancreatic and duodenal neuroendocrine tumors differ in their transcription factor signatures: expression of ISL1, PDX1, NGN3, and CDX2

We recently identified the transcription factor (TF) islet 1 gene product (ISL1) as a marker for well-differentiated pancreatic neuroendocrine tumors (P-NETs). In order to better understand the expression of the four TFs, ISL1, pancreatico-duodenal homeobox 1 gene product (PDX1), neurogenin 3 gene product (NGN3), and CDX-2 homeobox gene product (CDX2), that mainly govern the development and differentiation of the pancreas and duodenum, we studied their expression in hormonally defined P-NETs and duodenal (D-) NETs. Thirty-six P-NETs and 14 D-NETs were immunostained with antibodies against the four pancreatic hormones, gastrin, serotonin, calcitonin, ISL1, PDX1, NGN3, and CDX2. The TF expression pattern of each case was correlated with the tumor's hormonal profile. Insulin-positive NETs expressed only ISL1 (10/10) and PDX1 (9/10). Glucagon-positive tumors expressed ISL1 (7/7) and were almost negative for the other TFs. Gastrin-positive NETs, whether of duodenal or pancreatic origin, frequently expressed PDX1 (17/18), ISL1 (14/18), and NGN3 (14/18). CDX2 was mainly found in the gastrin-positive P-NETs (5/8) and rarely in the D-NETs (1/10). Somatostatin-positive NETs, whether duodenal or pancreatic in origin, expressed ISL1 (9/9), PDX1 (3/9), and NGN3 (3/9). The remaining tumors showed labeling for ISL1 in addition to NGN3. There was no association between a particular TF pattern and NET features such as grade, size, location, presence of metastases, and functional activity. We conclude from our data that there is a correlation between TF expression patterns and certain hormonally defined P-NET and D-NET types, suggesting that most of the tumor types originate from embryologically determined precursor cells. The observed TF signatures do not allow us to distinguish P-NETs from D-NETs.

Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.