Clustering-Based Inter-Regional Correlation Estimation

A novel non-parametric estimator of the correlation between grouped measurements of a quantity is proposed in the presence of noise. This work is primarily motivated by functional brain network construction from fMRI data, where brain regions correspond to groups of spatial units, and correlation between region pairs defines the network. The challenge resides in the fact that both noise and intra-regional correlation lead to inconsistent inter-regional correlation estimation using classical approaches. While some existing methods handle either one of these issues, no non-parametric approaches tackle both simultaneously. To address this problem, we propose a trade-off between two procedures: correlating regional averages, which is not robust to intra-regional correlation; and averaging pairwise inter-regional correlations, which is not robust to noise. To that end, we project the data onto a space where Euclidean distance is used as a proxy for sample correlation. We then propose to leverage hierarchical clustering to gather together highly correlated variables within each region prior to inter-regional correlation estimation. We provide consistency results, and empirically show our approach surpasses several other popular methods in terms of quality. We also provide illustrations on real-world datasets that further demonstrate its effectiveness

A Mixed Model Approach for Estimating Regional Functional Connectivity from Voxel-level BOLD Signals

Resting state brain functional connectivity quantifies the similarity between brain regions, each of which consists of voxels at which dynamic signals are acquired via neuroimaging techniques such as blood-oxygen-level-dependent signals in functional magnetic resonance imaging. Pearson correlation and similar metrics have been adopted by neuroscientists to estimate inter-regional connectivity, usually after averaging of signals within regions. However, dependencies between signals within each region and the presence of noise could contaminate such inter-regional correlation estimates. We propose a mixed-effects model with a novel covariance structure that explicitly isolates the different sources of variability in the observed BOLD signals, including correlated regional signals, local spatiotemporal variability, and measurement error. Methods for tackling the computational challenges associated with restricted maximum likelihood estimation will be discussed. Large sample properties are discussed and used for uncertainty quantification. Simulation results demonstrate that the parameters of the proposed model parameters can be accurately estimated and is superior to the Pearson correlation of averages in the presence of spatiotemporal noise. The proposed model is also applied to a real data set of BOLD signals collected from rats to construct individual brain networks.Comment: 25 pages, 6 figure

Load‐induced osteogenic differentiation of mesenchymal stromal cells is caused by mechano‐regulated autocrine signaling

Mechanical boundary conditions critically influence the bone healing process. In this context, previous in vitro studies have demonstrated that cyclic mechanical compression alters migration and triggers osteogenesis of mesenchymal stromal cells (MSC), both processes being relevant to healing. However, it remains unclear whether this mechanosensitivity is a direct consequence of cyclic compression, an indirect effect of altered supply or a specific modulation of autocrine bone morphogenetic protein (BMP) signaling. Here, we investigate the influence of cyclic mechanical compression (ε = 5% and 10%, f = 1 Hz) on human bone marrow MSC (hBMSC) migration and osteogenic differentiation in a 3D biomaterial scaffold, an in vitro system mimicking the mechanical environment of the early bone healing phase. The open-porous architecture of the scaffold ensured sufficient supply even without cyclic compression, minimizing load-associated supply alterations. Furthermore, a large culture medium volume in relation to the cell number diminished autocrine signaling. Migration of hBMSCs was significantly downregulated under cyclic compression. Surprisingly, a decrease in migration was not associated with increased osteogenic differentiation of hBMSCs, as the expression of RUNX2 and osteocalcin decreased. In contrast, BMP2 expression was significantly upregulated. Enabling autocrine stimulation by increasing the cell-to-medium ratio in the bioreactor finally resulted in a significant upregulation of RUNX2 in response to cyclic compression, which could be reversed by rhNoggin treatment. The results indicate that osteogenesis is promoted by cyclic compression when cells condition their environment with BMP. Our findings highlight the importance of mutual interactions between mechanical forces and BMP signaling in controlling osteogenic differentiation

Social Interventions Targeting Social Relations Among Older People at Nursing Homes:A Qualitative Synthesized Systematic Review

Social relations are part of the complex set of factors affecting health and well-being in old age. This systematic review seeks to uncover whether social interventions have an effect on social and health-related measures among nursing home residents. The authors screened PubMed, Scopus, and PsycINFO for relevant peer-reviewed literature. Interventions were included if (1) they focused primarily on social relations or related terms such as loneliness, social support, social isolation, social network, or being involuntarily alone either as the base theory of the intervention or as an outcome measure of the intervention; (2) they were implemented at nursing homes (or similar setting); (3) they had a narrative activity as its core (as opposed to dancing, gardening or other physical activity); (4) their participants met either physically or nonphysically, ie, via video-conference or the like; and if (5) they targeted residents at a nursing home. The authors systematically appraised the quality of the final selection of studies using the Mixed Methods Assessments Tool (MMAT) version 2011 and did a qualitative synthesis of the final study selection. A total of 10 studies were included. Reminiscence therapy was the most common intervention. Studies also included video-conference, cognitive, and support group interventions. All studies found the social interventions brought about positive trends on either/or the social and health-related measures included. Despite limited and very diverse evidence, our systematic review indicated a positive social and health-related potential of social interventions for older people living in nursing homes or similar institutions

Feather corticosterone levels on wintering grounds have no carry-over effects on breeding among three populations of great skuas (<i>Stercorarius skua</i>)

Environmental conditions encountered by migratory seabirds in their wintering areas can shape their fitness. However, the underlying physiological mechanisms remain largely unknown as birds are relatively inaccessible during winter. To assess physiological condition during this period, we measured corticosterone concentrations in winter-grown primary feathers of female great skuas (Stercorarius skua) from three breeding colonies (Bjørnøya, Iceland, Shetland) with wintering areas identified from characteristic stable isotope signatures. We subsequently compared winter feather corticosterone levels between three wintering areas (Africa, Europe and America). Among females breeding in 2009, we found significant differences in feather corticosterone levels between wintering areas. Surprisingly, levels were significantly higher in Africa despite seemingly better local ecological factors (based on lower foraging effort). Moreover, contrary to our predictions, females sharing the same wintering grounds showed significant differences in feather corticosterone levels depending on their colony of origin suggesting that some skuas could be using suboptimal wintering areas. Among females wintering in Africa, Shetland females showed feather corticosterone levels on average 22% lower than Bjørnøya and Iceland females. Finally, the lack of significant relationships between winter feather corticosterone levels and any of the breeding phenology traits does not support the hypothesis of potential carry-over effects of winter feather corticosterone. Yet, the fitness consequences of elevated feather corticosterone levels remain to be determined

Survival prognosis and variable selection: A case study for metastatic castrate resistant prostate cancer patients

Survival prognosis is challenging, and accurate prediction of individual survival times is often very difficult. Better statistical methodology and more data can help improve the prognostic models, but it is important that methods and data usages are evaluated properly. The Prostate Cancer DREAM Challenge offered a framework for training and blinded validation of prognostic models using a large and rich dataset on patients diagnosed with metastatic castrate resistant prostate cancer. Using the Prostate Cancer DREAM Challenge data we investigated and compared an array of methods combining imputation techniques of missing values for prognostic variables with tree-based and lasso-based variable selection and model fitting methods. The benchmark metric used was integrated AUC (iAUC), and all methods were benchmarked using cross-validation on the training data as well as via the blinded validation. We found that survival forests without prior variable selection achieved the best overall performance (cv-iAUC = 0.70, validation-iACU = 0.78), while a generalized additive model was best among those methods that used explicit prior variable selection (cv-iAUC = 0.69, validation-iACU = 0.76). Our findings largely concurred with previous results in terms of the choice of important prognostic variables, though we did not find the level of prostate specific antigen to have prognostic value given the other variables included in the data

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS