scholarly journals Altered resting‐state functional connectivity and effective connectivity of the habenula in irritable bowel syndrome: A cross‐sectional and machine learning study

2020 ◽  
Vol 41 (13) ◽  
pp. 3655-3666
Author(s):  
Cui P. Mao ◽  
Fen R. Chen ◽  
Jiao H. Huo ◽  
Liang Zhang ◽  
Gui R. Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Irritable Bowel Syndrome ◽  
Functional Connectivity ◽  
Resting State ◽  
Effective Connectivity ◽  
Learning Study ◽  
Resting State Functional Connectivity ◽  
Cross Sectional ◽  
Irritable Bowel

Tu1802 Patients With Irritable Bowel Syndrome Show Sex Related Differences in Resting-State Functional Connectivity

2014 ◽  
Vol 146 (5) ◽  
pp. S-847
Author(s):  
Jui-Yang Hong ◽  
Jennifer S. Labus ◽  
Lisa A. Kilpatrick ◽  
Jean Stains ◽  
Nuwanthi Heendeniya ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Irritable Bowel

scholarly journals A neuropsychosocial signature predicts longitudinal symptom changes in women with irritable bowel syndrome

2021 ◽  
Author(s):  
Ravi R. Bhatt ◽  
Arpana Gupta ◽  
Jennifer S. Labus ◽  
Cathy Liu ◽  
Priten P. Vora ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Functional Connectivity ◽  
Clinical Features ◽  
Resting State ◽  
Behavioral Therapy ◽  
Anatomical Connectivity ◽  
Data Types ◽  
Resting State Functional Connectivity ◽  
Brain Morphometry ◽  
Irritable Bowel

AbstractIrritable bowel syndrome (IBS) is a common disorder of brain-gut interactions characterized by chronic abdominal pain, altered bowel movements, often accompanied by somatic and psychiatric comorbidities. We aimed to test the hypothesis that a baseline phenotype composed of multi-modal neuroimaging and clinical features predicts clinical improvement on the IBS Symptom Severity Scale (IBS-SSS) at 3 and 12 months without any targeted intervention. Female participants (N = 60) were identified as “improvers” (50-point decrease on IBS-SSS from baseline) or “non-improvers.” Data integration analysis using latent components (DIABLO) was applied to a training and test dataset to determine whether a limited number of sets of multiple correlated baseline’omics data types, including brain morphometry, anatomical connectivity, resting-state functional connectivity, and clinical features could accurately predict improver status. The derived predictive models predicted improvement status at 3-months and 12-months with 91% and 83% accuracy, respectively. Across both time points, non-improvers were classified as having greater correlated morphometry, anatomical connectivity and resting-state functional connectivity characteristics within salience and sensorimotor networks associated with greater pain unpleasantness, but lower default mode network integrity and connectivity. This suggests that non-improvers have a greater engagement of attentional systems to perseverate on painful visceral stimuli, predicting IBS exacerbation. The ability of baseline multimodal brain-clinical signatures to predict symptom trajectories may have implications in guiding integrative treatment in the age of precision medicine, such as treatments targeted at changing attentional systems such as mindfulness or cognitive behavioral therapy.

scholarly journals Parental Education, Ethnicity, and Functional Connectivity between Nucleus Accumbens and Frontoparietal Network

2021 ◽  
Author(s):  
Shervin Assari
Keyword(s):  
Nucleus Accumbens ◽  
Functional Connectivity ◽  
Resting State ◽  
Parental Education ◽  
Black People ◽  
Right Hemisphere ◽  
Latino Youth ◽  
Resting State Functional Connectivity ◽  
Cross Sectional ◽  
Frontoparietal Network

While studies have indicated an association between socioeconomic status (SES) and neuroimaging measures, weaker SES effects are shown for Blacks than Whites. This is, in part, due to processes such as stratification, racism, minoritization, and othering of Black people in the United States. However, less is known about Latino youth. This study had two aims: First, to test the association between parental education and the right and left nucleus accumbens (NAcc) resting-state functional connectivity with the frontoparietal network (FPN) in children; and second, to investigate ethnic heterogeneity in this association. This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study. We analyzed the resting-state functional connectivity data (rsFC) of 10,840 US preadolescents who were between 9 and 10 years old. The main outcomes were the NAcc resting-state functional connectivity with FPN separately calculated for right and left hemispheres. Parental education was our independent variable. Family structure, sex, and age were covariates. Furthermore, ethnicity (Latino vs. non-Latino) was regarded as the moderator. We used mixed-effects regression for data analysis with and without interaction terms between parental education and ethnicity. Most participants (n = 8690; 80.2%) were non-Latino and 2150 (19.8%) were Latino. Parental education was associated with higher right and left NAcc resting-state functional connectivity with FPN. Ethnicity showed statistically significant interactions with parental education, suggesting that the positive associations between parental education and right and left NAcc resting-state functional connectivity with FPN were different in non-Latino and Latino children. For right hemisphere, we found significantly stronger and for left hemisphere, we found significantly weaker association for Latino compared with non-Latino preadolescents. Preadolescents’ NAcc resting-state functional connectivity with FPN depends on the intersections of ethnicity, parental education, and laterality.

scholarly journals Altered hypothalamus functional connectivity with limbic system in fibromyalgia and the modulation effect of intervention

2020 ◽  
Author(s):  
Jian Kong ◽  
Yiting Huang ◽  
Jiao Liu ◽  
Siyi Yu ◽  
Ming Cheng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Limbic System ◽  
Resting State ◽  
Tai Chi ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Trial Registration ◽  
Modulation Effect ◽  
Resting State Functional Connectivity ◽  
Before And After

Abstract Background: This study aims to investigate the resting state functional connectivity (rsFC) changes of the hypothalamus in Fibromyalgia patients and the modulation effect of effective treatments. Methods: Fibromyalgia patients and matched healthy controls (HC’s) were recruited. Resting state fMRI data were collected from fibromyalgia patients before and after a 12-week Tai Chi intervention and once from HC’s. Results: Data analysis showed that fibromyalgia patients displayed significantly decreased medial hypothalamus (MH) rsFC with the thalamus and amygdala when compared to HC’s at baseline. After the intervention, fibromyalgia patients showed increased (normalized) MH rsFC in the thalamus and amygdala. Effective connectivity analysis showed disrupted MH and thalamus interaction in fibromyalgia, which nonetheless could be partially restored by Tai Chi. Conclusions: Elucidating the role of the diencephalon and limbic system in the pathophysiology and development of fibromyalgia may facilitate the development of new treatment methods for this prevalent disorder. Trial registration: Trial registration ClinicalTrials.gov Identifier: NCT02407665. Registered 3 April 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02407665

scholarly journals Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Bidhan Lamichhane ◽  
Andy G. S. Daniel ◽  
John J. Lee ◽  
Daniel S. Marcus ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Functional Connectivity ◽  
Resting State ◽  
Learning Analytics ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Term Survival ◽  
Long Term Survival

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

scholarly journals The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

2019 ◽  
Author(s):  
Hannes Almgren ◽  
Frederik Van de Steen ◽  
Adeel Razi ◽  
Karl Friston ◽  
Daniele Marinazzo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Measurement Noise ◽  
Resting State Networks ◽  
Cross Sectional ◽  
Global Signal ◽  
Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

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