Assessing the influence of different ROI selection strategies on functional connectivity analyses of fMRI data acquired during steady-state conditions

In blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), assessing functional connectivity between and within brain networks from datasets acquired during steady-state conditions has become increasingly common. However, in contrast to connectivity analyses based on task-evoked signal changes, selecting the optimal spatial location of the regions of interest (ROIs) whose timecourses will be extracted and used in subsequent analyses is not straightforward. Moreover, it is also unknown how different choices of the precise anatomical locations within given brain regions influence the estimates of functional connectivity under steady-state conditions. The objective of the present study was to assess the variability in estimates of functional connectivity induced by different anatomical choices of ROI locations for a given brain network. We here targeted the default mode network (DMN) sampled during both resting-state and a continuous verbal 2-back working memory task to compare four different methods to extract ROIs in terms of ROI features (spatial overlap, spatial functional heterogeneity), signal features (signal distribution, mean, variance, correlation) as well as strength of functional connectivity as a function of condition. We show that, while different ROI selection methods produced quantitatively different results, all tested ROI selection methods agreed on the final conclusion that functional connectivity within the DMN decreased during the continuous working memory task compared to rest.     

Influence of different urinary bladder filling levels and controlling regions of interest selection on deformable image registration algorithms

PurposeEvaluation of Raystation ANAtomically CONstrained Deformation Algorithm (ANACONDA) performance to different urinary bladder filling levels in male pelvis anatomic site varying the controlling Regions Of Interest (ROIs).MethodsDifferent image datasets were obtained with ImSimQA (Oncology System Limited, Shrewsbury, UK) to evaluate ANACONDA performances (RaySearch Laboratories, Stockholm, Sweden). Deformation vector fields were applied to a synthetic man pelvis and a real patient computed tomography (CT) dataset (reference CTs) resulting in deformed CTs (target CTs) with various bladder filling levels. Different deformable image registrations (DIRs) were generated between each target CTs and reference CTs varying the controlling ROIs subset. Deformed ROIs were mapped from target CT to reference CT and then compared to reference ROIs. Evaluation was performed by Dice Similarity Coefficient (DSC), Correlation Coefficient (CC), Mean Distance to Agreement (MDA), maximum Distance to Agreement (maxDA) and with the introduction of global DSC (global_DSC) and global CC (global_CC) parameters.ResultsIn both synthetic and real patient CT cases, DSC scored less than 0.75 and MDA greater than 3 mm when no ROIs or only bladder were exploited as controlling ROI. DSC and CC increased by increasing the number of controlling ROIs selected whereas, an opposite behavior was observed for MDA and maxDA.ConclusionsANACONDA performances can be influenced by bladder filling fluctuation if no controlling ROIs are selected. Global_DSC and global_CC are useful parameters to quantitatively compare DIR algorithms. DIR performances improve by increasing the number of controlling ROIs selected, reaching a saturation level after a defined ROIs subset selection.     

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer''s disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer''s Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer''s disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.     

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer

New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley’s K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the findings from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.     

Intra-Arterial MR Perfusion Imaging of Meningiomas: Comparison to Digital Subtraction Angiography and Intravenous MR Perfusion Imaging

MethodsStudies were performed in a suite comprised of an x-ray angiography unit and 1.5T MR scanner that permitted intraprocedural patient movement between the imaging modalities. Patients underwent intra-arterial (IA) and intravenous (IV) T2* dynamic susceptibility MR perfusion immediately prior to meningioma embolization. Regional tumor arterial supply was characterized by digital subtraction angiography and classified as external carotid artery (ECA) dural, internal carotid artery (ICA) dural, or pial. MR perfusion data regions of interest (ROIs) were analyzed in regions with different vascular supply to extract peak height, full-width at half-maximum (FWHM), relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), and mean transit time (MTT). Linear mixed modeling was used to identify perfusion curve parameter differences for each ROI for IA and IV MR imaging techniques. IA vs. IV perfusion parameters were also directly compared for each ROI using linear mixed modeling.Results18 ROIs were analyzed in 12 patients. Arterial supply was identified as ECA dural (n = 11), ICA dural (n = 4), or pial (n = 3). FWHM, rCBV, and rCBF showed statistically significant differences between ROIs for IA MR perfusion. Peak Height and FWHM showed statistically significant differences between ROIs for IV MR perfusion. RCBV and MTT were significantly lower for IA perfusion in the Dural ECA compared to IV perfusion. Relative CBF in IA MR was found to be significantly higher in the Dural ICA region and MTT significantly lower compared to IV perfusion.     

Graph hierarchies for phylogeography

Bayesian phylogeographic methods simultaneously integrate geographical and evolutionary modelling, and have demonstrated value in assessing spatial spread patterns of measurably evolving organisms. We improve on existing phylogeographic methods by combining information from multiple phylogeographic datasets in a hierarchical setting. Consider N exchangeable datasets or strata consisting of viral sequences and locations, each evolving along its own phylogenetic tree and according to a conditionally independent geographical process. At the hierarchical level, a random graph summarizes the overall dispersion process by informing which migration rates between sampling locations are likely to be relevant in the strata. This approach provides an efficient and improved framework for analysing inherently hierarchical datasets. We first examine the evolutionary history of multiple serotypes of dengue virus in the Americas to showcase our method. Additionally, we explore an application to intrahost HIV evolution across multiple patients.     

Influence of rearing system,diet and gender on performance,carcass traits and meat quality of Polish Landrace pigs

Forty-eight Polish Landrace pigs (initially 32.43 ± 0.70 kg live weight) were assigned to a 2 × 2 × 2 factorial arrangement in order to assess the effects of the rearing system (outdoor v. indoor), diet (with corn grain silage v. without corn grain silage) and gender (barrows v. gilts) on performance, carcass traits, meat quality and intramuscular collagen (IMC) characteristics. During the trial period of 14 weeks, each group was housed in a pen of 12 m², and the outdoor-reared animals had access to a paddock of 24 m². Pigs were fed a diet computed according to standard requirements and supplied on a basis of 9% of metabolic weight (live weight^0.75). Pigs were slaughtered at 110.69 ± 0.85 kg. The rearing system did not significantly affect growth, hot and cold dressing, meat quality of m. longissimus lumborum or IMC. Outdoor pigs had less (P < 0.05) backfat thickness, slightly higher (P = 0.082) lean percent and a meat with lower pH and fat than the conventional system. In light of these results, alternative pig rearing systems with indoor space and free outdoor access could be an interesting production system for the pigs. Compared with the pigs fed diets with corn grain silage, those fed diets without corn had higher (P < 0.05) lean, ham percentage and IMC content, lower (P < 0.05) neck percentage but similar main tissue components of the ham and meat quality. Compared with the gilts, barrows were fatter and showed a meat with different physico-chemical traits, which was slightly more tender and had similar IMC properties.     

Similarity analysis for DNA sequences based on chaos game representation. Case study: The albumin

Using chaos game representation we introduce a novel and straightforward method for identifying similarities/dissimilarities between DNA sequences of the same type, from different organisms. A matrix is associated to each CGR pattern and the similarities result from the comparison between the matrices of the sequences of interest. Three different methods of analysis of the resulting difference matrix are considered: a 3-dimensional representation giving both local and global information, a numerical characterization by defining an n-letter word similarity measure and a statistical evaluation. The method is illustrated by implementation to the study of albumin nucleotides sequences from eight mammal species taking as reference the human albumin.     

Harmonizing finite element modelling for non-invasive strength estimation by high-resolution peripheral quantitative computed tomography

The finite element (FE) method based on high-resolution peripheral quantitative computed tomography (HR-pQCT) use a variety of tissue constitutive properties and boundary conditions at different laboratories making comparison of mechanical properties difficult. Furthermore, the advent of a second-generation HR-pQCT poses challenges due to improved resolution and a larger region of interest (ROI). This study addresses the need to harmonize results across FE models. The aims are to establish the relationship between FE results as a function of boundary conditions and a range of tissue properties for the first-generation HR-pQCT system, and to determine appropriate model parameters for the second-generation HR-pQCT system. We implemented common boundary conditions and tissue properties on a large cohort (N = 1371), and showed the relationships were highly linear (R² > 0.99) for yield strength and reaction force between FE models. Cadaver radii measured on both generation HR-pQCT with matched ROIs were used to back-calculate a tissue modulus that accounts for the increased resolution (61 µm versus 82 µm), resulting in a modulus of 8748 MPa for second-generation HR-pQCT to produce bone yield strength and reaction force equivalent to using 6829 MPa for first-generation HR-pQCT. Finally, in vivo scans (N = 61) conducted on both generations demonstrated that the larger ROI in the second-generation system results in stronger bone outcome measures, suggesting it is not advisable to convert FE results across HR-pQCT generations without matching ROIs. Together, these findings harmonize FE results by providing a means to compare findings with different boundary conditions and tissue properties, and across scanner generations.     

Thermal body patterns for healthy Brazilian adults (male and female)

The aim of this study was to establish the skin temperature (T_sk) thermal profile for the Brazilian population and to compare the differences between female and male Brazilian adults. A total of 117 female and 103 male were examined with a thermographic camera. The T_sk of 24 body regions of interest (ROI) were recorded and analyzed. Male T_sk results were compared to female and 10 ROI were evaluated with respect to the opposite side of the body (right vs. left) to identify the existence of significant contralateral T_sk differences (ΔT_sk). When compared right to left, the largest contralateral ΔT_sk was 0.3 °C. The female vs. male analysis yielded significant differences (p<0.05) in 13 of the 24 ROI. Thigh regions, both ventral and dorsal, had the highest ΔT_sk by sex (≈1.0 °C). T_sk percentile below P₅ or P₁₀ and over P₉₀ or P₉₅ may be used to characterize hypothermia and hyperthermia states, respectively. Thermal patterns and T_sk tables were established for Brazilian adult men and women for each ROI. There is a low T_sk variation between sides of the body and gender differences were only significant for some ROIs.     

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Background

Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them.

Methods

We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects.

Results

Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as “hubs” for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property.In particular, a higher local efficiency E _l can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz).By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions.

Conclusions

The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

     

Étude de la variabilité de la clairance hépatique de la mébrofénine-99mTc en scintigraphie hépatobiliaire

AimThe purpose of this study was to investigate some of the parameters likely to influence mebrofenin-^99mTc hepatic clearance calculation and inter-and intra-observers reproducibility.Materials and methodsHepatic clearance (%/min m²) of 30 scintigraphies was calculated from the values of hepatic, cardiac, and total activities, according to the method recommended in the literature. We studied: 1) impact of injection–acquisition delay variations; 2) acquisition type: anterior face only (FA) or geometric mean (GM); 3) clearances calculated according to four different body surface area (BSA) formulas; 4) intra-and inter-observers reproducibility for three observers (two evaluations for each observer).Results1) Clearance differences between different studied intervals were statistically significant, more important if the studied interval was far from reference interval (150–350 secondes) and even more when the interval studied was too early (110–310 secondes). 2) There was a statistically significant difference between clearance calculated using either FA or GM datasets (0.85 %/min m²). 3) There were small but statistically significant differences for four of the clearance comparisons using different BSA formulas. 4) Despite differences in size of cardiac and hepatic regions of interest (ROI), intra-observer reproducibility of hepatic clearance was excellent for each observer. Inter-observers reproducibility was also excellent (r = 0.982).ConclusionHepatic clearance of mebrofenin-^99mTc appears to be a highly reproducible method provided that acquisition and clearance calculation are standardized. It provides additionnal functional information to morphological and biological data usually performed before major hepatectomy. Thereby, the definition of a standardized protocol would enable realization of multicentric studies.     

Regional peak plantar pressures are highly sensitive to region boundary definitions

Traditional pedobarographic analyses subsample pressure data over a number of discrete anatomical regions of interest (ROIs). To our knowledge, the sensitivity of these data to ROI boundary definitions has not been previously addressed. Eight subjects each performed 20 trials of self-paced walking; commercial software was used to define 10 ROIs for each of the 160 total peak pressure images, and regional peak pressures (RPPs) were extracted for each image (total: 1600 values). We then asked three specific questions regarding RPP sensitivity to ROI boundary definition: (1) Is the ROI centroid representative of the RPP location? (2) How frequently do RPPs lie on the ROI boundary? and (3) By how much do RPP values change if the ROI boundary is changed by one pixel (resolution: 5.08×7.62 mm)? We found that the RPP locations differed from the ROI centroid in 80% of the cases and that the RPPs lay on the ROI boundary with a probability of 65%. We also found that a single-pixel change in the ROI boundary caused a mean RPP change of 10.8%. The most sensitive region was the midfoot for which a single-pixel ROI change yielded a median 29.4% change in RPP. These results indicate that RPP data are biased by regionalization schemes, which delineate pressure fields based on anatomy rather than on the field's geometric properties, and ultimately that regionalization may constitute a poor method of quantifying complex pressure fields. RPP sensitivity should be considered when making statistical inferences regarding foot function.     

On dose cube pixel spacing pre-processing for features extraction stability in dosiomic studies

PurposeDosiomics allows to parameterize regions of interest (ROIs) and to produce quantitative dose features encoding the spatial and statistical distribution of radiotherapy dose. The stability of dosiomics features extraction on dose cube pixel spacing variation has been investigated in this study.Material and MethodsBased on 17 clinical delivered dose distributions (P_n), dataset has been generated considering all the possible combinations of four dose grid resolutions and two calculation algorithms. Each dose voxel cube has been post-processed considering 4 different dose cube pixel spacing values: 1x1x1, 2x2x2, 3x3x3 mm³ and the one equal to the planning CT. Dosiomics features extraction has been performed from four different ROIs. The stability of each extracted dosiomic feature has been analyzed in terms of coefficient of variation (CV) intraclass correlation coefficient (ICC).ResultsThe highest CV mean values were observed for PTV ROI and for the grey level size zone matrix features family. On the other hand, the lowest CV mean values have been found for RING ROI for the grey level co-occurrence matrix features family. P₃ showed the highest percentage of CV >1 (1.14%) followed by P₁₅ (0.41%), P₁ (0.29%) and P13 (0.19%). ICC analysis leads to identify features with an ICC >0.95 that could be considered stable to use in dosiomic studies when different dose cube pixel spacing are considered, especially the features in common among the seventeen plans.ConclusionConsidering the observed variability, dosiomic studies should always provide a report not only on grid resolution and algorithm dose calculation, but also on dose cube pixel spacing.     

Structure and Growth of the Leeward Kohala Field System: An Analysis with Directed Graphs

This study illustrates how the theory of directed graphs can be used to investigate the structure and growth of the leeward Kohala field system, a traditional Hawaiian archaeological site that presents an unparalleled opportunity to investigate relative chronology. The relative chronological relationships of agricultural walls and trails in two detailed study areas are represented as directed graphs and then investigated using graph theoretic concepts including cycle, level, and connectedness. The structural properties of the directed graphs reveal structure in the field system at several spatial scales. A process of deduction yields a history of construction in each detailed study area that is different than the history produced by an earlier investigation. These results indicate that it is now possible to study the structure and growth of the entire field system remnant using computer software implementations of graph theoretic concepts applied to observations of agricultural wall and trail intersections made on aerial imagery and/or during fieldwork. A relative chronology of field system development with a resolution of one generation is a possible result.     

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

PurposeSegmentation of cardiac sub-structures for dosimetric analyses is usually performed manually in time-consuming procedure. Automatic segmentation may facilitate large-scale retrospective analysis and adaptive radiotherapy. Various approaches, among them Hierarchical Clustering, were applied to improve performance of atlas-based segmentation (ABS).MethodsTraining dataset of ABS consisted of 36 manually contoured CT-scans. Twenty-five cardiac sub-structures were contoured as regions of interest (ROIs). Five auto-segmentation methods were compared: simultaneous automatic contouring of all 25 ROIs (Method-1); automatic contouring of all 25 ROIs using lungs as anatomical barriers (Method-2); automatic contouring of a single ROI for each contouring cycle (Method-3); hierarchical cluster-based automatic contouring (Method-4); simultaneous truth and performance level estimation (STAPLE). Results were evaluated on 10 patients. Dice similarity coefficient (DSC), average Hausdorff distance (AHD), volume comparison and physician score were used as validation metrics.ResultsAtlas performance improved increasing number of atlases. Among the five ABS methods, Hierarchical Clustering workflow showed a significant improvement maintaining a clinically acceptable time for contouring. Physician scoring was acceptable for 70% of the ROI automatically contoured. Inter-observer evaluation showed that contours obtained by Hierarchical Clustering method are statistically comparable with them obtained by a second, independent, expert contourer considering DSC. Considering AHD, distance from the gold standard is lower for ROIs segmented by ABS.ConclusionsHierarchical clustering resulted in best ABS results for the primarily investigated platforms and compared favorably to a second benchmark system. Auto-contouring of smaller structures, being in range of variation between manual contourers, may be ideal for large-scale retrospective dosimetric analysis.     

Coverage and error models of protein-protein interaction data by directed graph analysis

Using a directed graph model for bait to prey systems and a multinomial error model, we assessed the error statistics in all published large-scale datasets for Saccharomyces cerevisiae and characterized them by three traits: the set of tested interactions, artifacts that lead to false-positive or false-negative observations, and estimates of the stochastic error rates that affect the data. These traits provide a prerequisite for the estimation of the protein interactome and its modules.