scholarly journals The Effect of Grain and Grass Fed Beef and Chicken Breast Consumption on the Functional Connectivity in the Brain Using Resting State Functional Magnetic Resonance Imaging

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
E. S. Beyer ◽  
M. F. Miller ◽  
T. H. Davis ◽  
J. F. Legako
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Chemical Components ◽  
Volatile Components ◽  
Functional Brain ◽  
Dimethyl Sulfone ◽  
True Value ◽  
Significant Difference ◽  
The Brain

ObjectivesUnderstanding functional connectivity after consuming meat can be essential to fully understanding consumer’s preferences and the connection to certain flavor compounds. The objective of this study was to determine differences in the functional brain connectivity of consumers after consuming grass-fed beef, grain-fed beef and chicken while determining the different chemical and volatile components that differentiate the treatments.Materials and MethodsGrass-fed strip steaks, Grain-fed strip steaks and chicken breasts were collected, aged 21 d and cut into 1×1-inch consumer steaks. Each steak was vacuum sealed with a random identification number and frozen at –20°C. 23 volunteered consumers evaluated each treatment randomly followed by a Blood Oxygen Level-Dependent (BOLD) fMRI scan. Each consumer received a resting state scan and three scans following each sample. The beef was cooked to a medium degree of doneness (71°C) and the chicken was cooked to a well-done degree of doneness (75°C), followed by a 1-min resting period. The consumers were asked to complete a sensory ballot for each sample to quantify tenderness, juiciness, flavor, overall liking and quality. Each attribute was evaluated on a 100mm line scale. The sensory ballot, volatile and fatty acid data were analyzed by ANOVA and multiple means comparison using SAS while the fMRI data were analyzed using FSL’s FEAT software.ResultsThe results indicated all treatments were equal for tenderness and flavor, but the chicken was the least juicy (P < 0.05) and the grain-fed steak was ranked higher for overall liking (P < 0.05) in comparison to chicken. Furthermore, based on an independent component analysis, there was a significant difference in the functional connectivity (P < 0.05) from the resting state scan to all three treatments within the insular, medial prefrontal cortex, and amygdala regions. Additionally, there were significant differences in connectivity (P < 0.05) between the insula and orbitofrontal cortex in grass-fed compared to grain-fed beef. These areas are involved in processing sensory characteristics related to smell and taste and tend to track differences in preferences and stimulus value. Also, the samples were evaluated for volatile compounds with GC–MS and fatty acids using the FAMES method. Chicken and grass-fed beef was found to have a higher concentration (P < 0.05) of dimethyl sulfone in comparison to grain-fed beef, while the grass-fed steaks possessed a higher concentration (P < 0.05) of toluene in comparison to grain-fed steaks, but not differing from chicken. Dimethyl sulfone and toluene have been tied to grass-fed beef and chicken flavor profiles (Tansawat et al., 2013).ConclusionThe results from the functional brain connectivity in the reward pathways and the chemical components of the different treatments indicated a trend for grain-fed beef to be the most different from grass-fed beef and chicken. Moreover, tying brain activity to the flavor and chemical components in meat can be vital in understanding consumer’s preferences not observed in behavior alone. Therefore, these results can provide a basis to determine the ability to track reactions within the functional connectivity in the brain and the chemical aspects of different steaks to determine and understand consumer’s preferences and the true value of beef and chicken.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 128 ◽  
Author(s):  
Aline Viol ◽  
Fernanda Palhano-Fontes ◽  
Heloisa Onias ◽  
Draulio de Araujo ◽  
Philipp Hövel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Structural Information ◽  
Brain Connectivity ◽  
Functional Brain ◽  
States Of Consciousness ◽  
Functional Brain Networks ◽  
Empirical Networks ◽  
Emergent Behaviors

With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

scholarly journals The Influence of Heart Rate Variability Biofeedback on Cardiac Regulation and Functional Brain Connectivity

2021 ◽  
Vol 15 ◽  
Author(s):  
Andy Schumann ◽  
Feliberto de la Cruz ◽  
Stefanie Köhler ◽  
Lisa Brotte ◽  
Karl-Jürgen Bär
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Control Group ◽  
Functional Brain ◽  
Middle Cingulate Cortex

BackgroundHeart rate variability (HRV) biofeedback has a beneficial impact on perceived stress and emotion regulation. However, its impact on brain function is still unclear. In this study, we aimed to investigate the effect of an 8-week HRV-biofeedback intervention on functional brain connectivity in healthy subjects.MethodsHRV biofeedback was carried out in five sessions per week, including four at home and one in our lab. A control group played jump‘n’run games instead of the training. Functional magnetic resonance imaging was conducted before and after the intervention in both groups. To compute resting state functional connectivity (RSFC), we defined regions of interest in the ventral medial prefrontal cortex (VMPFC) and a total of 260 independent anatomical regions for network-based analysis. Changes of RSFC of the VMPFC to other brain regions were compared between groups. Temporal changes of HRV during the resting state recording were correlated to dynamic functional connectivity of the VMPFC.ResultsFirst, we corroborated the role of the VMPFC in cardiac autonomic regulation. We found that temporal changes of HRV were correlated to dynamic changes of prefrontal connectivity, especially to the middle cingulate cortex, the left insula, supplementary motor area, dorsal and ventral lateral prefrontal regions. The biofeedback group showed a drop in heart rate by 5.2 beats/min and an increased SDNN as a measure of HRV by 8.6 ms (18%) after the intervention. Functional connectivity of the VMPFC increased mainly to the insula, the amygdala, the middle cingulate cortex, and lateral prefrontal regions after biofeedback intervention when compared to changes in the control group. Network-based statistic showed that biofeedback had an influence on a broad functional network of brain regions.ConclusionOur results show that increased heart rate variability induced by HRV-biofeedback is accompanied by changes in functional brain connectivity during resting state.

scholarly journals A Study Over Brain Connectivity Network of Parkinson's Patients, Using Nonparametric Bayesian Model

2021 ◽  
pp. 1-18
Author(s):  
Fatemeh Pourmotahari ◽  
◽  
Seyyed Mohammad Tabatabaei ◽  
Nasrin Borumandnia ◽  
Naghmeh Khadembashi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Functional Connectivity ◽  
Correlation Coefficient ◽  
Resting State ◽  
Bayesian Model ◽  
Functional Relationship ◽  
Brain Connectivity ◽  
Healthy Individuals ◽  
Nonparametric Bayesian ◽  
The Brain

Introduction: Parkinson’s disease is a neurodegenerative disease that disrupts functional brain networks. Many neurodegenerative disorders are associated with changes in brain communication patterns. Resting-state functional connectivity studies can distinguish the topological structure of Parkinson's patients from healthy individuals by analyzing patterns between different regions of the brain. Accordingly, the present study aimed to determine the brain topological features and functional connectivity in patients with Parkinson's disease, using a Bayesian approach. Method: The data of this study were downloaded from the open neuro site. These data include "Resting-State Functional MRI" (rs-fMRI) of 11 healthy individuals and 11 Parkinson’s patients with mean ages of 64.36 and 63.73, respectively. An advanced nonparametric Bayesian model was used to evaluate topological characteristics, including clustering of brain regions and correlation coefficient of the clusters. The significance of functional relationships based on each edge between the two groups was examined through false discovery rate (FDR) and network-based statistics (NBS) methods. Results: Brain connectivity results showed a major difference in terms of the number of regions in each cluster and the correlation coefficient between the patient and healthy groups. The largest clusters in the patient and control groups were 26 and 53 regions, respectively, with clustering correlation values of 0.36 and 0.26. Although there are 15 common areas across the two clusters, the intensity of the functional relationship between these areas was different in the two groups. Moreover, using NBS and FDR methods, no significant difference was observed for each edge between the patient and healthy groups (p-value>0.05). Conclusion: The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions revisited

2019 ◽  
Author(s):  
Janine D. Bijsterbosch ◽  
Christian F. Beckmann ◽  
Mark W. Woolrich ◽  
Stephen M. Smith ◽  
Samuel J. Harrison
Keyword(s):  
Functional Connectivity ◽  
Network Structure ◽  
Resting State ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Spatial Effects ◽  
Functional Brain ◽  
Dual Regression ◽  
The Relationship

AbstractIn our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.Impact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

scholarly journals Functional Brain Connectivity Patterns Associated with Visual Hallucinations in Dementia with Lewy Bodies

2021 ◽  
pp. 1-10
Author(s):  
Stefania Pezzoli ◽  
Matteo De Marco ◽  
Giovanni Zorzi ◽  
Annachiara Cagnin ◽  
Annalena Venneri
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dementia With Lewy Bodies ◽  
Brain Connectivity ◽  
Lewy Bodies ◽  
Resting State Fmri ◽  
Group Differences ◽  
Visual Hallucinations ◽  
Functional Brain ◽  
Functional Brain Networks

Background: The presence of recurrent, complex visual hallucinations (VH) is among the core clinical features of dementia with Lewy bodies (DLB). It has been proposed that VH arise from a disrupted organization of functional brain networks. However, studies are still limited, especially investigating the resting-state functional brain features underpinning VH in patients with dementia. Objective: The aim of the present pilot study was to investigate whether there were any alterations in functional connectivity associated with VH in DLB. Methods: Seed-based analyses and independent component analysis (ICA) of resting-state fMRI scans were carried out to explore differences in functional connectivity between DLB patients with and without VH. Results: Seed-based analyses reported decreased connectivity of the lateral geniculate nucleus, the superior parietal lobule and the putamen with the medial frontal gyrus in DLB patients with VH. Visual areas showed a pattern of both decreased and increased functional connectivity. ICA revealed between-group differences in the default mode network (DMN). Conclusion: Functional connectivity analyses suggest dysfunctional top-down and bottom-up processes and DMN-related alterations in DLB patients with VH. These impairments might foster the generation of false visual images that are misinterpreted, ultimately resulting in VH.

scholarly journals Learning differentially reorganizes brain activity and connectivity

2020 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
Azeez Adebimpe ◽  
Ankit N. Khambhati ◽  
Marcelo G. Mattar ◽  
Daniel Romer ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Learning Performance ◽  
Functional Architecture ◽  
Time Space ◽  
Novel Objects ◽  
Value Learning ◽  
The Brain

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity—as a function of time, space, and performance—that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.

scholarly journals Disrupted functional connectivity of the brain reward system in substance use problems: a meta-analysis of functional neuroimaging studies

2021 ◽  
Author(s):  
Jules R. Dugré ◽  
Pierre Orban ◽  
Stéphane Potvin
Keyword(s):  
Substance Use ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Reward System ◽  
Functional Brain ◽  
Substance Use Problems ◽  
Brain Reward ◽  
The Brain ◽  
Brain Reward System

ABSTRACTImportanceExtensive literature suggests that the brain reward system is crucial in understanding the neurobiology of substance use disorders. However, across studies on substance use problems, evidence of reliable disruptions in functional connectivity is limited.ObjectiveTo uncover deficient functional connectivity with the brain reward system that are reliably associated with substance use problems, by meta-analytically synthesizing results of functional brain connectivity studies on substance use problems.Data SourcesIdentification of relevant functional brain connectivity studies on substance misuse was done using PubMed, Google Scholar and EMBASE (until September 2021) with the following terms: cannabis, cocaine, substance, methamphetamine, amphetamine, alcohol, tobacco, nicotine, functional connectivity, resting-state, task-based connectivity, psychophysiological interaction.Study SelectionGuidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses were followed, Publications were included if they reported stereotactic coordinates of functional brain connectivity results on individuals with substance use problems without a comorbid major mental illness or organic impairment.Data Extraction and SynthesisSpatially convergent brain regions across functional connectivity studies on subjects with substance use problems were analyzed using Activation Likelihood Estimation meta-analysis.Altered connectivity with regions of the brain reward system was performed carried out through voxelwise seed-based meta-analyses. Subanalyses were performed to examine mediating factors such as severity of illness, connectivity modalities and types of substances.Main Outcomes and MeasuresIdentification of deficits in functional brain connectivity with the reward system across studies on substance use problems.ResultsNinety-six studies using a seed-based connectivity approach were included, representing 5757 subjects with substance use problems. In subjects with substance use problems, the ventromedial prefrontal cortex exhibited hyperconnectivity with the ventral striatum, and hypoconnectivity with the amygdala and hippocampus. Executive striatum showed hyperconnectivity with motor thalamus and dorsolateral prefrontal cortex, and hypoconnectivity with anterior cingulate cortex and anterior insula. Finally, the limbic striatum was found to be hyperconnected to the orbitofrontal cortex, and hypoconnected to the precuneus, compared to healthy subjects.Conclusions and RelevanceThe current study provided meta-analytical evidence of deficient functional connectivity between brain regions of the reward system and cortico-striato-thalamocortical loops in addiction, in line with current influential neurobiological models. These results are consistent with deficits in motivation and habit formation occurring in addiction, and they also highlight alterations in brain regions involved in socio-emotional processing and attention salience.KEY POINTSQuestionWhat functional brain connectivities with the brain reward system are reliably disrupted across studies on substance use problems?FindingsSubjects with substance use problems exhibited deficient connectivity between the ventromedial prefrontal cortex and subcortical structures including the ventral striatum, amygdala, and hippocampus. Executive striatum showed hyperconnectivity with motor thalamus and dorsolateral prefrontal cortex, and hypoconnectivity with anterior cingulate cortex and anterior insula. Altered connectivity between limbic striatum and core regions of the default mode network was also observed.MeaningDeficient functional brain connectivity along the cortico-striato-thalamocortical loops may reflect deficits in habit formation, socio-emotional and salience processing in addiction.

scholarly journals The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity

2020 ◽  
Author(s):  
Andy Schumann ◽  
Feliberto de la Cruz ◽  
Stefanie Köhler ◽  
Lisa Brotte ◽  
Karl-Jürgen Bär
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Anterior Insula ◽  
Control Group ◽  
Functional Brain

AbstractBackgroundHeart rate variability (HRV) biofeedback has a beneficial impact on perceived stress and emotion regulation. However, its impact on brain function is still unclear. In this study, we aimed to investigate the effect of an 8-week HRV-biofeedback intervention on functional brain connectivity in healthy subjects.MethodsHRV biofeedback was carried out in five sessions per week, including four at home and one in our lab. A control group played jump‘n’run games instead of the training. Functional magnetic resonance imaging was conducted before and after the intervention in both groups. To compute resting state functional connectivity (RSFC), we defined regions of interest in the ventral medial prefrontal cortex (VMPFC) and a total of 260 independent anatomical regions for network-based analysis. Changes of RSFC of the VMPFC to other brain regions were compared between groups. Temporal changes of HRV during the resting state recording were correlated to dynamic functional connectivity of the VMPFC.ResultsFirst, we corroborated the role of the VMPFC in cardiac autonomic regulation. We found that temporal changes of HRV were correlated to dynamic changes of prefrontal connectivity, especially to the middle cingulate cortex, left anterior insula, right amygdala, supplementary motor area, dorsal and ventral lateral prefrontal regions. The biofeedback group showed a drop in heart rate by 5.5 beats/min and an increased RMSSD as a measure of HRV by 10.1ms (33%) after the intervention. Functional connectivity of the VMPFC increased mainly to the right anterior insula, the dorsal anterior cingulate cortex and the dorsolateral prefrontal cortex after biofeedback intervention when compared to changes in the control group. Network-based statistic showed that biofeedback had an influence on a broad functional network of brain regions.ConclusionOur results show that increased vagal modulation induced by HRV-biofeedback is accompanied by changes in functional brain connectivity during resting state.

scholarly journals Interactive effects of the APOE and BDNF polymorphisms on functional brain connectivity: the Tasmanian Healthy Brain Project

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Manuela Pietzuch ◽  
Aidan Bindoff ◽  
Sharna Jamadar ◽  
James C. Vickers
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Premotor Cortex ◽  
Occipital Cortex ◽  
Interactive Effects ◽  
Parahippocampal Gyrus ◽  
Functional Brain ◽  
Potential Biomarker ◽  
Dorsal Attention Network

AbstractResting-state functional magnetic resonance imaging measures pathological alterations in neurodegenerative diseases, including Alzheimer’s disease. Disruption in functional connectivity may be a potential biomarker of ageing and early brain changes associated with AD-related genes, such as APOE and BDNF. The objective of this study was to identify group differences in resting-state networks between individuals with BDNF Val66Met and APOE polymorphisms in cognitively healthy older persons. Dual regression following Independent Components Analysis were performed to examine differences associated with these polymorphisms. APOE ε3 homozygotes showed stronger functional connectivity than APOE ε4 carriers. Males showed stronger functional connectivity between the Default Mode Network (DMN) and grey matter premotor cortex, while females showed stronger functional connectivity between the executive network and lateral occipital cortex and parahippocampal gyrus. Additionally, we found that with increasing cognitive reserve, functional connectivity increased within the Dorsal Attention Network (DAN), but decreased within the DMN. Interaction effects indicated stronger functional connectivity in Met/ε3 carriers than in Met/ε4 and Val/ε4 within both the DMN and DAN. APOE/BDNF interactions may therefore influence the integrity of functional brain connections in older adults, and may underlie a vulnerable phenotype for subsequent Alzheimer’s-type dementia.

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