scholarly journals Brain network topology predicts participant adherence to mental training programs

2020 ◽  
Vol 4 (3) ◽  
pp. 528-555
Author(s):  
Marzie Saghayi ◽  
Jonathan Greenberg ◽  
Christopher O’Grady ◽  
Farshid Varno ◽  
Muhammad Ali Hashmi ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Local Level ◽  
Resting State Fmri ◽  
Brain Network ◽  
Learning Performance ◽  
Local Network ◽  
P Value ◽  
Mental Training ◽  
Wide Range ◽  
Organizational Patterns

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity.

scholarly journals BRAPH: A Graph Theory Software for the Analysis of Brain Connectivity

2017 ◽  
Author(s):  
Mite Mijalkov ◽  
Ehsan Kakaei ◽  
Joana B. Pereira ◽  
Eric Westman ◽  
Giovanni Volpe ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Local Network ◽  
Healthy Controls ◽  
The Brain

AbstractThe brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH – BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

scholarly journals Brain Network Organization Correlates with Autistic Features in Preschoolers with Autism Spectrum Disorders and in Their Fathers: Preliminary Data from a DWI Analysis

2019 ◽  
Vol 8 (4) ◽  
pp. 487 ◽  
Author(s):  
Billeci ◽  
Calderoni ◽  
Conti ◽  
Lagomarsini ◽  
Narzisi ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Brain Network ◽  
Local Network ◽  
Network Organization ◽  
Frontal Pole ◽  
Structural Network ◽  
Spectrum Disorders

Autism Spectrum Disorders (ASD) is a group of neurodevelopmental disorders that is characterized by an altered brain connectivity organization. Autistic traits below the clinical threshold (i.e., the broad autism phenotype; BAP) are frequent among first-degree relatives of subjects with ASD; however, little is known regarding whether subthreshold behavioral manifestations of ASD mirror also at the neuroanatomical level in parents of ASD probands. To this aim, we applied advanced diffusion network analysis to MRI of 16 dyads consisting of a child with ASD and his father in order to investigate: (i) the correlation between structural network organization and autistic features in preschoolers with ASD (all males; age range 1.5–5.2 years); (ii) the correlation between structural network organization and BAP features in the fathers of individuals with ASD (fath-ASD). Local network measures significantly correlated with autism severity in ASD children and with BAP traits in fath-ASD, while no significant association emerged when considering the global measures of brain connectivity. Notably, an overlap of some brain regions that are crucial for social functioning (cingulum, superior temporal gyrus, inferior temporal gyrus, middle frontal gyrus, frontal pole, and amygdala) in patients with ASD and fath-ASD was detected, suggesting an intergenerational transmission of these neural substrates. Overall, the results of this study may help in elucidating the neurostructural endophenotype of ASD, paving the way for bridging connections between underlying genetic and ASD symptomatology.

scholarly journals Individual Variation in Brain Network Topology Predicts Emotional Intelligence

2018 ◽  
Author(s):  
Ling George ◽  
Lee Ivy ◽  
Guimond Synthia ◽  
Lutz Olivia ◽  
Tandon Neeraj ◽  
...  
Keyword(s):  
Emotional Intelligence ◽  
Social Cognition ◽  
Network Topology ◽  
Resting State ◽  
Large Scale ◽  
Psychotic Disorders ◽  
Brain Connectivity ◽  
Distance Matrix ◽  
Resting State Fmri ◽  
Brain Network

AbstractBackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

scholarly journals Regression dynamic causal modeling for resting-state fMRI

2020 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Computationally Efficient ◽  
Dynamic Causal Modeling ◽  
Whole Brain ◽  
Wide Range

Abstract“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks.Here, we show that a method recently developed for task-fMRI – regression dynamic causal modeling (rDCM) – extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

scholarly journals Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder

2020 ◽  
Vol 4 (4) ◽  
pp. 1219-1234 ◽  
Author(s):  
Emily Marshall ◽  
Jason S. Nomi ◽  
Bryce Dirks ◽  
Celia Romero ◽  
Lauren Kupis ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Network Dynamics ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Children With Asd ◽  
Transient Activation

Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static measures of functional connectivity. Recent work has focused on identifying transient configurations of brain activity, yet several open questions remain regarding the nature of specific brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP) approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 children (ages 6–13 years; n = 75 ASD; n = 138 male). Following brain parcellation by using independent component analysis, dynamic CAP analyses were conducted and k-means clustering was used to determine transient activation patterns of the M-CIN. The frequency of occurrence of different dynamic CAP brain states was then compared between children with ASD and typically developing (TD) children. Dynamic brain configurations characterized by coactivation of the M-CIN with central executive/lateral fronto-parietal and default mode/medial fronto-parietal networks appeared less frequently in children with ASD compared with TD children. This study highlights the utility of time-varying approaches for studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate that altered M-CIN dynamics in ASD may underlie the inflexible behaviors commonly observed in children with the disorder.

scholarly journals Altered resting state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

2018 ◽  
Author(s):  
Caroline Garcia Forlim ◽  
Leonie Klock ◽  
Johanna Baechle ◽  
Laura Stoll ◽  
Patrick Giemsa ◽  
...  
Keyword(s):  
Resting State ◽  
Local Level ◽  
Resting State Fmri ◽  
Brain Network ◽  
Graph Analysis ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Schizophrenic Patients ◽  
Brain Circuitry ◽  
Behavioral Impairments

Schizophrenia is described as a disease in which complex psychopathology together with cognitive and behavioral impairments are related to widely disrupted brain circuitry causing a failure in coordinating information across multiple brain sites. This led to the hypothesis of schizophrenia as a network disease e.g. in the cognitive dysmetria model and the dysconnectivity theory. Nevertheless, there is no consensus regarding localized mechanisms, namely dysfunction of certain networks underlying the multifaceted symptomatology. In this study, we investigated potential functional disruptions in 35 schizophrenic patients and 41 controls using complex cerebral network analysis, namely network-based statistic (NBS) and graph theory in resting state fMRI. NBS can reveal locally impaired subnetworks whereas graph analysis characterizes whole brain network topology. Using NBS we observed a local hyperconnected thalamo-cortico-cerebellar subnetwork in the schizophrenia group. Furthermore, nodal graph measures retrieved from the thalamo-cortico-cerebellar subnetwork revealed that the total number of connections from/to (degree) of the thalamus is higher in patients with schizophrenia. Interestingly, graph analysis on the whole brain functional networks did not reveal group differences. Together, our results suggest that disruptions in the brain networks of schizophrenia patients are situated at the local level of the hyperconnected thalamo-cortico-cerebellar rather than globally spread in brain. Our results provide further evidence for the importance of the thalamus and cerebellum in schizophrenia and to the notion that schizophrenia is a network disease in line with the dysconnectivity theory and cognitive dysmetria model.

scholarly journals Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance

2017 ◽  
Author(s):  
Aurora I. Ramos-Nuñez ◽  
Simon Fischer-Baum ◽  
Randi Martin ◽  
Qiuhai Yue ◽  
Fengdan Ye ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Predicting Performance ◽  
Brain Network Connectivity ◽  
Two Measures ◽  
And Performance ◽  
Dynamic Measures ◽  
The Relationship

AbstractIn cognitive network neuroscience, the connectivity and community structure of the brain network is related to cognition. Much of this research has focused on two measures of connectivity – modularity and flexibility – which frequently have been examined in isolation. By using resting state fMRI data from 52 young adults, we investigate the relationship between modularity, flexibility and performance on cognitive tasks. We show that flexibility and modularity are highly negatively correlated. However, we also demonstrate that flexibility and modularity make unique contributions to explain task performance, with modularity predicting performance for simple tasks and flexibility predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain

scholarly journals Natural Experiment on Trade and Investment Liberalization and Soft Drink Consumption

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
Y Tcholakov
Keyword(s):  
Public Health ◽  
International Trade ◽  
Natural Experiment ◽  
Soft Drink ◽  
Trade Agreement ◽  
P Value ◽  
Soft Drink Consumption ◽  
Obesity And Diabetes ◽  
Wide Range ◽  
The Impact

Abstract Background Globalization is recognized to as a contributing factor to a health harming environment through a variety of mechanisms including through changes in food systems and food availability. Sugar-sweetened beverage (SSB) consumption is linked to obesity and diabetes and its regulation is a key priority for public health. The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is an international trade agreement between 11 countries. Methods This project uses of natural experiment methods to predict the impact of the entry into force of the CPTPP on SSB consumption. These methods allow quantitative inferences to be drawn in the situations where the exposure is not randomly assigned. Soft drink consumption data was collected from the Euromonitor database for 80 countries from all regions. This data was used to estimate the effect of agreements similar to the TPP. Results Eleven country trade agreement pairs were identified. In 5 cases out of the 11, the exposed country had a higher soft drink consumption at five years after the trade agreement. The effect of the trade agreement exposure for an average country in the sample in a trade agreement was found to be 1.10 (95% CI: 1.01-1.18; p-value: 0.03) after adjusting for GDP and the involvement of the US. In 7 of the 11 member-countries soft drink consumption is expected to increase yielding an average increase of 9.0% in those countries; the changes did not yield statistically significant differences in others. Conclusions This projected extended the use of synthetic methods to the projection of future effects of policy implementation. While it showed that there may be increasing trend of SSB consumption in certain scenarios, this could not be generalized to all cases. This illustrates the wide range of effects of international trade liberalization and highlights that national policy probably plays a strong modulating role on the impact that it has on local food environments. Key messages Globalization can lead to health harming environments and its impacts should further be studied by public health professionals and researchers. Many global policies have the potential to lead to significant health impacts but are negotiated without involving public health experts.

scholarly journals Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Jin Li ◽  
◽  
Chenyuan Bian ◽  
Dandan Chen ◽  
Xianglian Meng ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Genetic Risk ◽  
Brain Connectivity ◽  
Persistent Homology ◽  
Brain Network ◽  
Resonance Imaging ◽  
Modeling Framework ◽  
Apoe Ε4 ◽  
Topological Features

Abstract Background Although genetic risk factors and network-level neuroimaging abnormalities have shown effects on cognitive performance and brain atrophy in Alzheimer’s disease (AD), little is understood about how apolipoprotein E (APOE) ε4 allele, the best-known genetic risk for AD, affect brain connectivity before the onset of symptomatic AD. This study aims to investigate APOE ε4 effects on brain connectivity from the perspective of multimodal connectome. Results Here, we propose a novel multimodal brain network modeling framework and a network quantification method based on persistent homology for identifying APOE ε4-related network differences. Specifically, we employ sparse representation to integrate multimodal brain network information derived from both the resting state functional magnetic resonance imaging (rs-fMRI) data and the diffusion-weighted magnetic resonance imaging (dw-MRI) data. Moreover, persistent homology is proposed to avoid the ad hoc selection of a specific regularization parameter and to capture valuable brain connectivity patterns from the topological perspective. The experimental results demonstrate that our method outperforms the competing methods, and reasonably yields connectomic patterns specific to APOE ε4 carriers and non-carriers. Conclusions We have proposed a multimodal framework that integrates structural and functional connectivity information for constructing a fused brain network with greater discriminative power. Using persistent homology to extract topological features from the fused brain network, our method can effectively identify APOE ε4-related brain connectomic biomarkers.

Sign in / Sign up

Export Citation Format

Share Document