Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Multi-study evaluation of neuroimaging-based prediction of medication class in mood disorders.

Clinicians often face a dilemma in diagnosing bipolar disorder patients with complex symptoms who spend more time in a depressive state than a manic state. The current gold standard for such diagnosis, the Diagnostic and Statistical Manual (DSM), is not objectively grounded in pathophysiology. In such complex cases, relying solely on the DSM may result in misdiagnosis as major depressive disorder (MDD). A biologically-based classification algorithm that can accurately predict treatment response may help patients suffering from mood disorders. Here we used an algorithm to do so using neuroimaging data. We used the neuromark framework to learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework achieves up to 95.45% accuracy, 0.90 sensitivity, and 0.92 specificity in predicting antidepressant (AD) vs. mood stabilizer (MS) response in patients. We incorporated two additional datasets to evaluate the generalizability of our approach. The trained algorithm achieved up to 89% accuracy, 0.88 sensitivity, and 0.89 specificity in predicting the DSM-based diagnosis on these datasets. We also translated the model to distinguish responders to treatment from nonresponders with up to 70% accuracy. This approach reveals multiple salient biomarkers of medication-class of response within mood disorders.

Accuracy of Ultrasound Scans as Compared to Fine Needle Aspiration Cytology in the Diagnosis of Thyroid Nodules.

INTRODUCTION: Thyroid nodules (TNs) are among the more common findings on physical examinations. Due to the fear of the TN harboring malignancy and with the increasing incidence of thyroid cancer, ultrasound (US) scanning is used as an important diagnostic tool in the assessment of a TN. The American College of Radiology's Thyroid Imaging Reporting and Data System (TI-RADS) was established based on specific patterns composed of two or more features. According to the TI-RADS guidelines, a suspicious nodule by US findings should undergo fine-needle aspiration cytology (FNAC), in which results would guide further management. OBJECTIVE: This study was carried out to assess the accuracy of US as compared to FNAC in the diagnosis of a thyroid nodule. METHODOLOGY: This retrospective study involved 213 cases that were sent for FNAC after having done a US scan of the thyroid. Data was gathered from all patient files that were referred for FNAC thyroid between 01/02/2018 and 30/06/2021 in Al-Ahli Hospital in the state of Qatar. The US scans were interpreted and reported according to the TI-RADS criteria. The FNAC samples were interpreted and reported according to the Bethesda System for Reporting Thyroid Cytopathology. Data were tabulated and analyzed with Excel (Microsoft, Redmond, WA, USA) and SPSS version 25 (IBM Corp., Armonk, NY, USA). RESULTS: The study showed that US had a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 73.9%, 72.6%, 24.6% and 95.8%, respectively, with a significant association between the results of US and the results of FNAC (X2 (1, n = 213) = 20.295, p < .001) and a significant positive correlation (phi coefficient = .309, p < .001). In addition, the data showed that the odds for having a positive FNAC were 7.519 (95% CI: 2.811, 20.112) times greater for cases with positive US compared with cases with negative US. The relative risk of having a positive FNAC when the US was positive was 5.913 (95% CI: 2.440, 14.332) times greater compared to when the US was negative. CONCLUSION: While our results showed that US cannot be solely relied on in diagnosing TNs, they did show that US can reliably rule out a malignancy in TNs. Recent studies have been showing increasing accuracy of US in diagnosing TNs and more studies are needed to explore this topic.

Intestinal Stomas-Current Practice and Challenges: An Institutional Review.

Introduction: A stoma is an artificial anastomosis of the gastrointestinal tract to the abdominal skin wall to reroute the stream of feces. Fecal diversion, bowel decompression, and anastomosis protection are common indications for stomas. Relative to other surgical operations, stomas have a high morbidity rate, with rates averaging 40% and ranging 14-79%. The most common early complication was peristomal skin irritation. In contrast, parastomal hernias were the most common late complication. Methods: This research was performed at King Hamad University Hospital (KHUH) in the Kingdom of Bahrain. Our study included patients who had undergone ileostomies and colostomies. The inclusion criteria included adult patients who are 15 years and older, both emergency and elective cases, and with ASA score of 1-4. The excluded patients were those who had had their stomas performed outside of KHUH and those who were not following up in the hyperbaric department of our hospital. This study was performed using a retrospective study design. The sample size was 98 which included patients with stomas that were following up with the hyperbaric team between January 2018 and February 2021. Results: We have broken down the indications for stoma formation. The breakdown of all our documented complications are illustrated in the given figure. Conclusion: Within our institutional study, 63.3% of stoma complications consisted of skin problems. This formed the majority of complications. Establishing a stoma care unit would offer continuous support and care to patients and help them in returning to an optimal quality of life. Additionally, this goal can be met through preoperative and postoperative education regarding surgery and stoma formation. This includes preoperative stoma marking and siting, as well as improved recovery through instruction from knowledgeable stoma care specialists regarding hands-on stoma care.Finally, patients can be assisted through specialized stoma clinics. How to cite this article: Qassim T, Saeed MF, Qassim A, et al. Intestinal Stomas-Current Practice and Challenges: An Institutional Review. Euroasian J Hepato-Gastroenterol 2023;13(2):115-119.

Functional Connectivity Stability: A Signature of Neurocognitive Development and Psychiatric Problems in Children.

Brain functional connectivity has been shown to provide a type of fingerprint for adult subjects. However, most studies tend to focus on the connectivity strength rather than its stability across scans. In this study, we performed for the first time a large-scale analysis of within-individual stability of functional connectivity (FC) using 9071 children from the Adolescent Brain Cognitive Development database. Functional network connectivity (FNC) was extracted via a fully automated independent component analysis framework. We found that children's FNC is robust and stable with high similarity across scans and serves as a fingerprint that can identify an individual child from a large group. The robustness of this finding is supported by replicating the identification in the two-year follow-up session and between longitudinal sessions. More interestingly, we discovered that the within-individual FNC stability was predictive of cognitive performance and psychiatric problems in children, with higher FNC stability correlating with better cognitive performance and fewer dimensional psychopathology. The overall results indicate that the FNC of children also shows reliable within-individual stability, acting as a fingerprint for distinguishing participants, regardless of significant growth and development in the children's brain. FC stability can be a valuable imaging marker to predict early cognitive and psychiatric behaviors in children. Clinical Relevance---The stability of functional connectivity can be used to identify children from a large group and to draw inferences on early-age cognitive and psychiatric behaviors.

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network's learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

An Approach to Automatically Label and Order Brain Activity/Component Maps.

Background: Functional magnetic resonance imaging (fMRI) is a brain imaging technique that provides detailed insights into brain function and its disruption in various brain disorders. The data-driven analysis of fMRI brain activity maps involves several postprocessing steps, the first of which is identifying whether the estimated brain network maps capture signals of interest, for example, intrinsic connectivity networks (ICNs), or artifacts. This is followed by linking the ICNs to standardized anatomical and functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary to facilitate interpretation. Methods: Here we develop a novel and efficient method (Autolabeler) for implementing and integrating all of these processes in a fully automated manner. The Autolabeler method is pretrained on a cross-validated elastic-net regularized general linear model from the noisecloud toolbox to separate neuroscientifically meaningful ICNs from artifacts. It is capable of automatically labeling activity maps with labels from several well-known anatomical and functional parcellations. Subsequently, this method also maximizes the modularity within functional domains to generate a more systematically structured FNC matrix for post hoc network analyses. Results: Results show that our pretrained model achieves 86% accuracy at classifying ICNs from artifacts in an independent validation data set. The automatic anatomical and functional labels also have a high degree of similarity with manual labels selected by human raters. Discussion: At a time of ever-increasing rates of generating brain imaging data and analyzing brain activity, the proposed Autolabeler method is intended to automate such analyses for faster and more reproducible research. Impact statement Our proposed method is capable of implementing and integrating some of the crucial tasks in functional magnetic resonance imaging (fMRI) studies. It is the first to incorporate such tasks without the need for expert intervention. We develop an open-source toolbox for the proposed method that can function as stand-alone software and additionally provides seamless integration with the widely used group independent component analysis for fMRI toolbox (GIFT). This integration can aid investigators to conduct fMRI studies in an end-to-end automated manner.

Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder.

Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.

COVID-19 versus SARS: A comparative review.

The two genetically similar severe acute respiratory syndrome coronaviruses, SARS-CoV-1 and SARS-CoV-2, have each been responsible for global epidemics of vastly different scales. Although both viruses arose from similar origins, they quickly diverged due to differences in their transmission dynamics and spectrum of clinical presentations. The potential involvement of multiple organs systems, including the respiratory, cardiac, gastrointestinal and neurological, during infection necessitates a comprehensive understanding of the clinical pathogenesis of each virus. The management of COVID-19, initially modelled after SARS and other respiratory illnesses, has continued to evolve as we accumulate more knowledge and experience during the pandemic, as well as develop new therapeutics and vaccines. The impact of these two coronaviruses has been profound for our health care and public health systems, and we hope that the lessons learned will not only bring the current pandemic under control, but also prevent and reduce the impact of future pandemics.

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.

Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advantage - representation learning. We conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for DL. Results show that if trained following prevalent DL practices, DL methods have the potential to scale particularly well and substantially improve compared to SML methods, while also presenting a lower asymptotic complexity in relative computational time, despite being more complex. We also demonstrate that DL embeddings span comprehensible task-specific projection spectra and that DL consistently localizes task-discriminative brain biomarkers. Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain.

NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders.

Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer's disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.

The use of radiological imaging alongside reverse transcriptase PCR in diagnosing novel coronavirus disease 2019: a narrative review.

The diagnosis of novel coronavirus disease 2019 (COVID-19) has been a challenge in many countries due to nonspecific symptoms and variable incubation period. The current reference test is reverse transcriptase PCR. Many studies have reported high sensitivities of CT scans and suggested that they can be used in the diagnosis of COVID-19 alongside reverse transcriptase PCR. The current data about CT scans are highly variable and incoherent. Therefore, new multicentric studies in different countries are needed to better understand the role of CT scans in COVID-19 diagnosis. In this report, we will discuss the clinical relevance of each test and the current Centers for Disease Control and Prevention and American College of Radiology recommendations regarding the use of imaging in the diagnosis of COVID-19.

Determining the number of states in dynamic functional connectivity using cluster validity indexes.

BACKGROUND: Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several cluster validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined. NEW METHOD: Currently employed indexes do not provide a crisp answer on what is the best number of clusters. In addition, there is a lack of CVI testing in the context of dFC data. This work tests a comprehensive set of twenty four cluster validity indexes applied to addiction data and suggest the best ones for clustering dynamic functional connectivity. RESULTS: Out of the twenty four considered CVIs, Davies-Bouldin and Ray-Turi were the most suitable methods to find the number of clusters in both simulation and real data. The solution for these two CVIs is to find a local minimum critical point, which can be automated using computational algorithms. COMPARISON WITH EXISTING METHODS: Elbow-Criterion, Silhouette and GAP-Statistic methods have been widely used in dFC studies. These methods are included among the tested CVIs where the performances of all twenty four CVIs are compared. CONCLUSIONS: Davies-Bouldin and Ray-Turi CVIs showed better performance among a group of twenty four CVIs in determining the number of clusters to use in dFC analysis.

Decreased Cross-Domain Mutual Information in Schizophrenia From Dynamic Connectivity States.

The study of dynamic functional network connectivity (dFNC) has been important to understand the healthy and diseased brain. Recent developments model groups of functionally related brain structures (defined as functional domains) as entities that can send and receive information. A domain analysis starts by detecting a finite set of connectivity patterns known as domain states within each functional domain. Dynamic functional domain connectivity (DFDC) is a novel information theoretic framework for studying the temporal sequence of the domain states and the amount of information shared among domains. In this setting, the information flow among functional domains can be compared to the flow of bits among entities in a digital network. Schizophrenia is a chronic psychiatric disorder which is associated with how the brain processes information. Here, we employed the DFDC framework to analyze a dataset containing resting-state fMRI scans from 163 healthy controls (HCs) and 151 schizophrenia patients (SZs). As in other information theory methods, this study measured domain state probabilities, entropy within each DFDC and the cross-domain mutual information (CDMI) between pairs of DFDC. Results indicate that SZs show significantly higher (transformed) entropy than HCs in subcortical (SC)-SC; default mode network (DMN)-visual (VIS) and frontoparietal (FRN)-VIS DFDCs. SZs also show lower (transformed) CDMI between SC-VIS vs. SC-sensorimotor (SM), attention (ATTN)-VIS vs. ATTN-SM and ATTN-SM vs. ATTN-ATTN DFDC pairs after correcting for multiple comparisons. These results imply that different DFDC pairs function in a more independent manner in SZs compared to HCs. Our findings present evidence of higher uncertainty and randomness in SZ brain function.

Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia.

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

Group ICA for identifying biomarkers in schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression.

Brain 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 ±â€¯8.9%, sensitivity 0.74 ±â€¯0.11, specificity 0.79 ±â€¯0.11) than STR (accuracy 67.45 ±â€¯8.13%, sensitivity 0.65 ±â€¯0.11, specificity 0.71 ±â€¯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.

Does the primary route of spread have a prognostic significance in stage III non-serous epithelial ovarian cancer?

BACKGROUND: The purpose of this retrospective study was to determine the prognosis of non-serous epithelial ovarian cancer (EOC) patients with exclusively retroperitoneal lymph node (LN) metastases, and to compare the prognosis of these women to that of patients who had abdominal peritoneal involvement. METHODS: A multicenter, retrospective department database review was performed to identify patients with stage III non-serous EOC at 7 gynecologic oncology centers in Turkey. Demographic, clinicopathological and survival data were collected. The patients were divided into three groups based on the initial sites of disease: 1) the retroperitoneal (RP) group included patients who had positive pelvic and /or para-aortic LNs only. 2) The intraperitoneal (IP) group included patients with > 2 cm IP dissemination outside of the pelvis. These patients all had a negative LN status, 3) The IP / RP group included patients with > 2 cm IP dissemination outside of the pelvis as well as positive LN status. Survival data were compared with regard to the groups. RESULTS: We identified 179 women with stage III non-serous EOC who were treated at 7 participating centers during the study period. The median age of the patients was 53 years, and the median duration of follow-up was 39 months. There were 35 (19.6%) patients in the RP group, 72 (40.2%) in the IP group and 72 (40.2%) in the IP/RP group. The 5-year disease-free survival (DFS) rates for the RP, the IP, and IP/RP groups were 66.4%, 37.6%, and 25.5%, respectively (p = 0.002). The 5-year overall survival (OS) rate for the RP group was significantly longer when compared to those of the IP, and the IP/RP groups (74.4% vs. 54%, and 36%, respectively; p = 0.011). However, we were not able to define "RP only disease" as an independent prognostic factor for increased DFS or OS. CONCLUSIONS: Primary non-serous EOC patients with node-positive-only disease seem to have better survival when compared to those with extra-pelvic peritoneal involvement.

Level of dietary energy and 2,4-thiazolidinedione alter molecular and systemic biomarkers of inflammation and liver function in Holstein cows.

BACKGROUND: The objective of the study was to evaluate the effect of overfeeding a moderate energy diet and a 2,4-thiazolidinedione (TZD) injection on blood and hepatic tissue biomarkers of lipid metabolism, oxidative stress, and inflammation as it relates to insulin sensitivity. RESULTS: Fourteen dry non-pregnant cows were fed a control (CON) diet to meet 100% of NRC requirements for 3 wk, after which half of the cows were assigned to a moderate-energy diet (OVE) and half of the cows continued on CON for 6 wk. All cows received an intravenous injection of 4 mg TZD/kg of body weight (BW) daily from 2 wk after initiation of dietary treatments and for 2 additional week. Compared with CON cows and before TZD treatment, the OVE cows had lower concentration of total protein, urea and albumin over time. The concentration of cholesterol and tocopherol was greater after 2 wk of TZD regardless of diet. Before and after TZD, the OVE cows had greater concentrations of AST/GOT, while concentrations of paraoxonase, total protein, globulin, myeloperoxidase, and haptoglobin were lower compared with CON cows. Regardless of diet, TZD administration increased the concentration of ceruloplasmin, ROMt, cholesterol, tocopherol, total protein, globulin, myeloperoxidase and beta-carotene. In contrast, the concentration of haptoglobin decreased at the end of TZD injection regardless of diet. Prior to TZD injection, the mRNA expression of PC, ANGPTL4, FGF21, INSR, ACOX1, and PPARD in liver of OVE cows was lower compared with CON cows. In contrast, the expression of HMGCS2 was greater in OVE compared with CON cows. After 1 wk of TZD administration the expression of IRS1 decreased regardless of diet; whereas, expression of INSR increased after 2 wk of TZD injection. Cows fed OVE had lower overall expression of TNF, INSR, PC, ACOX1, FGF21, and PPARD but greater HMGCS2 expression. These differences were most evident before and after 1 wk of TZD injection, and by 2 wk of TZD differences in expression for most genes disappeared. CONCLUSIONS: Based on molecular and blood data, administration of TZD enhanced some aspects of insulin sensitivity while causing contradictory results in terms of inflammation and oxidative stress. The bovine liver is TZD-responsive and level of dietary energy can modify the effects of TZD. Because insulin sensitizers have been proposed as useful tools to manage dairy cows during the transition period, further studies are required to investigate the potential hepatotoxicity effect of TZD (or similar compounds) in dairy cattle.

Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder.

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. © 2017 Wiley Periodicals, Inc.

Investigations into effects on performance and glutathione peroxidase activity in broilers when increasing selenium contents of complete diets appropriate to animals' selenium requirements by adding different selenium compounds (organic vs. inorganic).

The aim of this study was to compare the effects of inorganic and organic selenium compounds supplementations to diets containing adequate selenium in broilers on performance, carcass traits, plasma and tissue glutathione peroxidase activity. A total of 150 one-day-old broilers were randomized into one control and two treatment groups each containing 50 birds; each group was then divided into 3 replicate groups. The experiment lasted 42 days. All groups were fed with broiler starter diet from day 1 to 21 and finisher diet from day 22 to 42. The basal diet for control group included adequate selenium due to vitamin-mineral premix and feeds. The basal diet was supplemented with 0.2 mg/kg organic selenium (selenomethionine, treatment group 1) and 0.2 mg/kg inorganic selenium (sodium selenite, treatment group 2). Although no significant differences were determined between treatment group 1 and the control group for mean body weights, the differences between the group given inorganic selenium and the other groups were statistically significant (p < 0.01). There was no significant difference between control and treatment groups with regard to mean feed intake and feed efficiency. The dressing percentages of the second treatment group were found to be lower than the first treatment group. Treatment groups were observed to have increased levels of glutathione peroxidase in plasma (p <0.01), kidney (p < 0.05), femoral muscle (p < 0.05), heart (p < 0.01) and liver tissue (p < 0.01) compared with the control group. Results of this study indicated that the supplementation of organic selenium to diets containing adequate selenium increased plasma, liver, femoral muscle, kidney and heart tissue glutathione peroxidase activity in broilers.