scholarly journals Individual differences in aesthetic engagement are reflected in resting-state fMRI connectivity: Implications for stress resilience

NeuroImage ◽  
2018 ◽  
Vol 179 ◽  
pp. 156-165 ◽  
Author(s):  
Paula G. Williams ◽  
Kimberley T. Johnson ◽  
Brian J. Curtis ◽  
Jace B. King ◽  
Jeffrey S. Anderson
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Resting State Fmri ◽  
Stress Resilience ◽  
Aesthetic Engagement

scholarly journals Salience Network Connectivity Modulates Skin Conductance Responses in Predicting Arousal Experience

2017 ◽  
Vol 29 (5) ◽  
pp. 827-836 ◽  
Author(s):  
Chenjie Xia ◽  
Alexandra Touroutoglou ◽  
Karen S. Quigley ◽  
Lisa Feldman Barrett ◽  
Bradford C. Dickerson
Keyword(s):  
Individual Differences ◽  
Skin Conductance ◽  
Resting State ◽  
Subjective Experience ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Salience Network ◽  
Skin Conductance Responses ◽  
Additional Variance ◽  
Connectivity Strength

Individual differences in arousal experience have been linked to differences in resting-state salience network connectivity strength. In this study, we investigated how adding task-related skin conductance responses (SCR), a measure of sympathetic autonomic nervous system activity, can predict additional variance in arousal experience. Thirty-nine young adults rated their subjective experience of arousal to emotionally evocative images while SCRs were measured. They also underwent a separate resting-state fMRI scan. Greater SCR reactivity (an increased number of task-related SCRs) to emotional images and stronger intrinsic salience network connectivity independently predicted more intense experiences of arousal. Salience network connectivity further moderated the effect of SCR reactivity: In individuals with weak salience network connectivity, SCR reactivity more significantly predicted arousal experience, whereas in those with strong salience network connectivity, SCR reactivity played little role in predicting arousal experience. This interaction illustrates the degeneracy in neural mechanisms driving individual differences in arousal experience and highlights the intricate interplay between connectivity in central visceromotor neural circuitry and peripherally expressed autonomic responses in shaping arousal experience.

scholarly journals A distributed brain network predicts general intelligence from resting-state human neuroimaging data

2018 ◽  
Vol 373 (1756) ◽  
pp. 20170284 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Brain Volume ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Reliable Estimate ◽  
General Intelligence ◽  
Total Brain Volume ◽  
Human Connectome Project

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N= 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.

scholarly journals Threat Vigilance and Intrinsic Amygdala Connectivity

2022 ◽  
Author(s):  
Peter Kirk ◽  
Avram J Holmes ◽  
Oliver Joe Robinson
Keyword(s):  
Individual Differences ◽  
Trait Anxiety ◽  
Resting State ◽  
Resting State Fmri ◽  
Periaqueductal Gray ◽  
Behavioral Measure ◽  
Fmri Data ◽  
Vigilance Behavior ◽  
Prefrontal Circuitry ◽  
Threat Of Shock

A well documented amygdala-dorsomedial prefrontal circuit is theorized to promote attention to threat (‘threat vigilance’). Prior research has implicated a relationship between individual differences in trait anxiety/vigilance, engagement of this circuitry, and anxiogenic features of the environment (e.g. through threat-of-shock and movie-watching). In the present study, we predicted that—for those scoring high in self-reported anxiety and a behavioral measure of threat vigilance—this circuitry is chronically engaged, even in the absence of anxiogenic stimuli. Our analyses of resting-state fMRI data (N=639) did not, however, provide evidence for such a relationship. Nevertheless, in our planned exploratory analyses, we saw a relationship between threat vigilance behavior (but not self-reported anxiety) and intrinsic amygdala-periaqueductal gray connectivity. Here, we suggest this subcortical circuitry may be chronically engaged in hypervigilant individuals, but that the amygdala-prefrontal circuitry may only be engaged in response to anxiogenic stimuli.

scholarly journals A Neural Correlate of Strategic Exploration at the Onset of Adolescence

2016 ◽  
Vol 28 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Andrew S. Kayser ◽  
Zdeňa Op de Macks ◽  
Ronald E. Dahl ◽  
Michael J. Frank
Keyword(s):  
Individual Differences ◽  
Granger Causality ◽  
Resting State ◽  
Resting State Fmri ◽  
Neural Correlates ◽  
Fmri Data ◽  
Relative Uncertainty ◽  
Neural Basis ◽  
Neural Correlate

The onset of adolescence is associated with an increase in the behavioral tendency to explore and seek novel experiences. However, this exploration has rarely been quantified, and its neural correlates during this period remain unclear. Previously, activity within specific regions of the rostrolateral PFC (rlPFC) in adults has been shown to correlate with the tendency for exploration. Here we investigate a recently developed task to assess individual differences in strategic exploration, defined as the degree to which the relative uncertainty of rewards directs responding toward less well-evaluated choices, in 62 girls aged 11–13 years from whom resting state fMRI data were obtained in a separate session. Behaviorally, this task divided our participants into groups of explorers (n = 41) and nonexplorers (n = 21). When seed ROIs within the rlPFC were used to interrogate resting state fMRI data, we identified a lateralized connection between the rlPFC and posterior putamen/insula whose strength differentiated explorers from nonexplorers. On the basis of Granger causality analyses, the preponderant direction of influence may proceed from posterior to anterior. Together, these data provide initial evidence concerning the neural basis of exploratory tendencies at the onset of adolescence.

scholarly journals Predicting Individual Traits from Unperformed Tasks

2021 ◽  
Author(s):  
Shachar Gal ◽  
Niv Tik ◽  
Michal Bernstein-Eliav ◽  
Ido Tavor
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Cognitive Abilities ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Activation ◽  
Resting State Fmri ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Intelligence Scores

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

scholarly journals The individual functional connectome is unique and stable over months to years

2017 ◽  
Author(s):  
Corey Horien ◽  
Xilin Shen ◽  
Dustin Scheinost ◽  
R. Todd Constable
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
List Type ◽  
Functional Connectome ◽  
Wide Range ◽  
The Individual ◽  
Brain Functional Connectivity ◽  
Parietal Cortices

AbstractFunctional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across three months to three years. Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that the edges demonstrating the most individualized features tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and is not an idiosyncratic aspect of a specific dataset, but rather reflects stable individual differences in the functional connectivity of the brain.Research highlightsWhole-brain functional connectivity profiles obtained from four resting-state fMRI datasets are unique and stable across 3 months-3 years in adolescents, young adults, and older adultsMedial frontal and frontoparietal networks tended to be both unique and stableIndividual edges in the frontal and parietal cortices tended to be most discriminative of individual subjects

scholarly journals Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

2021 ◽  
Author(s):  
Ying-Qiu Zheng ◽  
Seyedeh-Rezvan Farahibozorg ◽  
Weikang Gong ◽  
Hossein Rafipoor ◽  
Saad Jbabdi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Predictive Accuracy ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Human Connectome Project ◽  
Wide Range ◽  
Dual Regression ◽  
Project Data ◽  
Test Retest Reliability

Modelling and predicting individual differences in task-evoked FMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble leaner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of tfMRI scans, suggesting that it has potential to supplement traditional task localisers.

Resting-state fMRI used for intraoperative neuronavigation in children

2013 ◽  
Vol 44 (S 01) ◽  
Author(s):  
C Dorfer ◽  
T Czech ◽  
G Kasprian ◽  
A Azizi ◽  
J Furtner ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri
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