scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

scholarly journals Linear Input as a Fundamental Motif of Brain Connectivity Organization

2019 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Fundamental Aspect ◽  
Biased Sampling ◽  
Whole Brain ◽  
3 Dimensional ◽  
Face Area ◽  
Learning Techniques

AbstractDescribing the pattern of region-to-region functional connectivity is an important step towards understanding information transfer and transformation between brain regions. Although fMRI data are limited in spatial resolution, recent advances in technology afford more precise mapping. Here, we extended previous methods, connective field mapping, to 3 dimensions to provide a more concise estimate of the organization and potential information transformation from one region to another. We first replicated previous work with the 3 dimensional model by showing that the topology of functional connectivity between early visual regions maintained along their eccentricity axis or the anterior-posterior dimension. We then examined higher order visual regions (e,g, fusiform face area) and showed that their pattern of connectivity, the convergence and biased sampling, seem to contribute to some of their core receptive field properties. We further demonstrated that linearity of input is a fundamental aspect of functional connectivity of the whole brain, with higher linearity between regions within a network than across networks; that is, high connective linearity was evident between early visual areas, and between prefrontal areas, but less evident between them. By decomposing the whole brain linearity matrix with manifold learning techniques, we found that the principle mode of the linearity maps onto decompositions in both functional connectivity and genetic expression reported in previous studies. The current work provides evidence supporting that linearity of input is likely a fundamental motif of functional connectivity between regions for information processing across the brain, with high linearity preserving the integrity of information from one region to another within a network.

scholarly journals Functional connectivity predicts the dispositional use of expressive suppression but not cognitive reappraisal

2019 ◽  
Author(s):  
Daisy A. Burr ◽  
Tracy d'Arbeloff ◽  
Maxwell Elliott ◽  
Annchen R. Knodt ◽  
Bartholomew D. Brigidi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Cognitive Reappraisal ◽  
Predictive Performance ◽  
Brain Regions ◽  
Expressive Suppression ◽  
Whole Brain ◽  
Functional Connections ◽  
Neural Signature

Previous research has identified specific brain regions associated with regulating emotion using common strategies such as expressive suppression and cognitive reappraisal. However, most research focuses on a priori regions and directs participants how to regulate, which may not reflect how people naturally regulate outside the laboratory. Here, we used a data-driven approach to investigate how individual differences in distributed intrinsic functional brain connectivity predict emotion regulation tendency. Specifically, we used connectome-based predictive modeling to extract functional connections in the brain significantly related to the dispositional use of suppression and reappraisal. These edges were then used in a predictive model and cross-validated in novel participants to identify a neural signature that reflects individual differences in the tendency to suppress and reappraise emotion. We found a significant neural signature for the dispositional use of suppression, but not reappraisal. Within this whole-brain signature, the intrinsic connectivity of the default mode network was most informative of suppression tendency. In addition, the predictive performance of this model was significant in males, but not females. These findings help inform how whole-brain networks of functional connectivity characterize how people tend to regulate emotion outside the laboratory.

scholarly journals Ultra-high-field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Enrico Schulz ◽  
Anne Stankewitz ◽  
Anderson M Winkler ◽  
Stephanie Irving ◽  
Viktor Witkovský ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Cognitive Strategies ◽  
Brain Regions ◽  
Imaging Data ◽  
Cold Pain ◽  
Whole Brain ◽  
Safe Place ◽  
Strength Change

We investigated how the attenuation of pain with cognitive interventions affects brain connectivity using neuroimaging and a whole brain novel analysis approach. While receiving tonic cold pain, 20 healthy participants performed three different pain attenuation strategies during simultaneous collection of functional imaging data at seven tesla. Participants were asked to rate their pain after each trial. We related the trial-by-trial variability of the attenuation performance to the trial-by-trial functional connectivity strength change of brain data. Across all conditions, we found that a higher performance of pain attenuation was predominantly associated with higher functional connectivity. Of note, we observed an association between low pain and high connectivity for regions that belong to brain regions long associated with pain processing, the insular and cingulate cortices. For one of the cognitive strategies (safe place), the performance of pain attenuation was explained by diffusion tensor imaging metrics of increased white matter integrity.

scholarly journals Transient patterns of functional dysconnectivity in youth with psychosis spectrum symptoms

2018 ◽  
Author(s):  
Eva Mennigen ◽  
Dietsje D. Jolles ◽  
Catherine E. Hegarty ◽  
Mohan Gupta ◽  
Maria Jalbrzikowski ◽  
...  
Keyword(s):  
Help Seeking ◽  
Large Scale ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Psychotic Symptoms ◽  
Whole Brain ◽  
State Dependent ◽  
Functional Brain Networks ◽  
Window Approach

AbstractPsychosis spectrum disorders are conceptualized as neurodevelopmental disorders accompanied by disruption of large-scale functional brain networks. Both static and dynamic dysconnectivity have been described in patients with schizophrenia and, more recently, in help-seeking individuals at clinical high-risk for psychosis. Less is known, however, about developmental aspects of dynamic functional network connectivity (FNC) associated with psychotic symptoms (PS) in the general population. Here, we investigate resting state fMRI data using established dynamic FNC methods in the Philadelphia Neurodevelopmental Cohort (ages 8-22), including 129 participants experiencing PS and 452 participants without PS (non-PS).Applying a sliding window approach and k-means clustering, 5 dynamic states with distinct whole-brain connectivity patterns were identified. PS-associated dysconnectivity was most prominent in states characterized by synchronization or antagonism of the default mode network (DMN) and cognitive control (CC) domains. Hyperconnectivity between DMN, salience, and CC domains in PS youth only occurred in a state characterized by synchronization of the DMN and CC domains, a state that also becomes less frequent with age. However, dysconnectivity of the sensorimotor and visual systems in PS youth was revealed in other transient states completing the picture of whole-brain dysconnectivity patterns associated with PS.Overall, state-dependent dysconnectivity was observed in PS youth, providing the first evidence that disruptions of dynamic functional connectivity are present across a broader psychosis continuum.

scholarly journals Resting-State Functional Connectivity of the Punishment Network Associated With Conformity

2020 ◽  
Vol 14 ◽  
Author(s):  
Yin Du ◽  
Yinan Wang ◽  
Mengxia Yu ◽  
Xue Tian ◽  
Jia Liu
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Three Phase ◽  
The Right ◽  
Global Brain

Fear of punishment prompts individuals to conform. However, why some people are more inclined than others to conform despite being unaware of any obvious punishment remains unclear, which means the dispositional determinants of individual differences in conformity propensity are poorly understood. Here, we explored whether such individual differences might be explained by individuals’ stable neural markers to potential punishment. To do this, we first defined the punishment network (PN) by combining all potential brain regions involved in punishment processing. We subsequently used a voxel-based global brain connectivity (GBC) method based on resting-state functional connectivity (FC) to characterize the hubs in the PN, which reflected an ongoing readiness state (i.e., sensitivity) for potential punishment. Then, we used the within-network connectivity (WNC) of each voxel in the PN of 264 participants to explain their tendency to conform by using a conformity scale. We found that a stronger WNC in the right thalamus, left insula, postcentral gyrus, and dACC was associated with a stronger tendency to conform. Furthermore, the FC among the four hubs seemed to form a three-phase ascending pathway, contributing to conformity propensity at every phase. Thus, our results suggest that task-independent spontaneous connectivity in the PN could predispose individuals to conform.

scholarly journals Impact of Binge Drinking During College on Resting State Functional Connectivity

2021 ◽  
Author(s):  
Tien T. Tong ◽  
Jatin G. Vaidya ◽  
John R. Kramer ◽  
Samuel Kuperman ◽  
Douglas R. Langbehn ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Binge Drinking ◽  
Resting State ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Functional Connection ◽  
Level Analysis

AbstractAimThe current study aimed to examine the longitudinal effects of standard binge drinking (4+/5+ drinks for females/males in 2 hours) and extreme binge drinking (8+/10+ drinks for females/males in 2 hours) on resting state functional connectivity.Method119 college students with distinct alcohol bingeing patterns (35 non-bingeing controls, 44 standard bingers, and 40 extreme bingers) were recruited to ensure variability in bingeing frequency. Resting state fMRI scans were obtained at time 1 when participants were college freshmen and sophomores and again approximately two years later. On four occasions during the 2-year period between scans, participants reported monthly standard and extreme binge drinking for the past 6 months. Association between bingeing and change in functional connectivity was studied using both network-level and edge-level analysis. Network connectivity was calculated by aggregating multiple edges (a functional connection between any two brain regions) affiliated with the same network. The network-level analysis used mixed-effects models to assess the association between standard/extreme binge drinking and change in network connectivity, focusing on canonical networks often implicated in substance misuse. On the other hand, the edge-level analysis tested the relationship between bingeing and change in whole-brain connectivity edges using connectome-based predictive modeling (CPM).ResultsFor network-level analysis, higher standard bingeing was associated with a decrease in connectivity between Default Mode Network-Ventral Attention Network (DMN-VAN) from time 1 to time 2, controlling for the initial binge groups at time 1, longitudinal network changes, in-scanner motion and other demographic covariates. For edge-level analysis, the CPM failed to identify a generalizable predictive model of cumulative standard/extreme bingeing from change in connectivity edges.ConclusionsOur findings suggest that binge drinking is associated with abnormality in networks implicated in attention allocation and self-focused processes, which, in turn, have been implicated in rumination, craving, and relapse. More extensive alterations in functional connectivity might be observed with heavier or longer binge drinking pattern.

scholarly journals Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

2016 ◽  
Author(s):  
Murat Demirtaş ◽  
Matthieu Gilson ◽  
John D. Murray ◽  
Dina Popovic ◽  
Eduard Vieta ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Computational Modeling ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Hemodynamic Response Function ◽  
Dynamic Functional Connectivity ◽  
Whole Brain ◽  
Global Average ◽  
Functional Connections

AbstractResting-state functional magnetic resonance imaging and diffusion weight imaging became a conventional tool to study brain connectivity in healthy and diseased individuals. However, both techniques provide indirect measures of brain connectivity leading to controversies on their interpretation. Among these controversies, interpretation of anti-correlated functional connections and global average signal is a major challenge for the field. In this paper, we used dynamic functional connectivity to calculate the probability of anti-correlations between brain regions. The brain regions forming task-positive and task-negative networks showed high anti-correlation probabilities. The fluctuations in anti-correlation probabilities were significantly correlated with those in global average signal and functional connectivity. We investigated the mechanisms behind these fluctuations using whole-brain computational modeling approach. We found that the underlying effective connectivity and intrinsic noise reflect the static spatiotemporal patterns, whereas the hemodynamic response function is the key factor defining the fluctuations in functional connectivity and anti-correlations. Furthermore, we illustrated the clinical implications of these findings on a group of bipolar disorder patients suffering a depressive relapse (BPD).

scholarly journals Changes in functional connectivity associated with facial expression processing over the working adult lifespan

2021 ◽  
Author(s):  
Thomas Murray ◽  
Justin O'Brien ◽  
Veena Kumari
Keyword(s):  
Functional Connectivity ◽  
Facial Expressions ◽  
Brain Connectivity ◽  
Negative Emotions ◽  
Brain Regions ◽  
Emotional Expressions ◽  
Expression Recognition ◽  
Frontal Pole ◽  
Adult Lifespan ◽  
Age Related

The recognition of negative emotions from facial expressions is shown to decline across the adult lifespan, with some evidence that this decline begins around middle age. While some studies have suggested ageing may be associated with changes in neural response to emotional expressions, it is not known whether ageing is associated with changes in the network connectivity associated with processing emotional expressions. In this study, we examined the effect of participant age on whole-brain connectivity to various brain regions that have been associated with connectivity during emotion processing: the left and right amygdalae, medial prefrontal cortex (mPFC), and right posterior superior temporal sulcus (rpSTS). The study involved healthy participants aged 20-65 who viewed facial expressions displaying anger, fear, happiness, and neutral expressions during functional magnetic resonance imaging (fMRI). We found effects of age on connectivity between the left amygdala and voxels in the occipital pole and cerebellum, between the right amygdala and voxels in the frontal pole, and between the rpSTS and voxels in the orbitofrontal cortex, but no effect of age on connectivity with the mPFC. Furthermore, ageing was more greatly associated with a decline in connectivity to the left amygdala and rpSTS for negative expressions in comparison to happy and neutral expressions, consistent with the literature suggesting a specific age-related decline in the recognition of negative emotions. These results add to the literature surrounding ageing and expression recognition by suggesting that changes in underlying functional connectivity might contribute to changes in recognition of negative facial expressions across the adult lifespan.

scholarly journals Functional Connectivity Network Disruption Underlies Domain-Specific Impairments in Attention for Children Born Very Preterm

2020 ◽  
Author(s):  
M D Wheelock ◽  
R E Lean ◽  
S Bora ◽  
T R Melzer ◽  
A T Eggebrecht ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Attention Problems ◽  
School Age Children ◽  
Prospective Longitudinal Study ◽  
Domain Specific ◽  
Very Preterm ◽  
Prospective Longitudinal

Abstract Attention problems are common in school-age children born very preterm (VPT; < 32 weeks gestational age), but the contribution of aberrant functional brain connectivity to these problems is not known. As part of a prospective longitudinal study, brain functional connectivity (fc) was assessed alongside behavioral measures of selective, sustained, and executive attention in 58 VPT and 65 full-term (FT) born children at corrected-age 12 years. VPT children had poorer sustained, shifting, and divided attention than FT children. Within the VPT group, poorer attention scores were associated with between-network connectivity in ventral attention, visual, and subcortical networks, whereas between-network connectivity in the frontoparietal, cingulo-opercular, dorsal attention, salience and motor networks was associated with attention functioning in FT children. Network-level differences were also evident between VPT and FT children in specific attention domains. Findings contribute to our understanding of fc networks that potentially underlie typical attention development and suggest an alternative network architecture may help support attention in VPT children.

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

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