Inter-individual differences in resting-state functional connectivity are linked to interval timing in irregular contexts

Cortex ◽  
2020 ◽  
Vol 128 ◽  
pp. 254-269 ◽  
Author(s):  
Alice Teghil ◽  
Antonella Di Vita ◽  
Fabrizia D'Antonio ◽  
Maddalena Boccia
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Interval Timing ◽  
Resting State Functional Connectivity

Resting-state functional connectivity in multiple sclerosis: An examination of group differences and individual differences

2013 ◽  
Vol 51 (13) ◽  
pp. 2918-2929 ◽  
Author(s):  
Alisha L. Janssen ◽  
Aaron Boster ◽  
Beth A. Patterson ◽  
Amir Abduljalil ◽  
Ruchika Shaurya Prakash
Keyword(s):  
Multiple Sclerosis ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Group Differences ◽  
Resting State Functional Connectivity

scholarly journals Individual differences in frontoparietal plasticity in humans

2021 ◽  
Author(s):  
Austin L Boroshok ◽  
Anne T Park ◽  
Panagiotis Fotiadis ◽  
Gerardo H Velasquez ◽  
Ursula A Tooley ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Animal Models ◽  
Neural Plasticity ◽  
Resting State ◽  
Response Times ◽  
Resting State Functional Connectivity ◽  
Short Term ◽  
Complex Cognition

Neuroplasticity, defined as the brain's ability to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here we investigated the plasticity of the frontoparietal system (FPS), which supports complex cognition. We asked whether VTA resting-state functional connectivity and myelin map (T1-w/T2-w ratio) values predicted learning after short-term training on a FPS-dependent task: the adaptive n-back (n = 46, ages 18-25). We found that stronger connectivity between VTA and lateral prefrontal cortex at baseline predicted greater improvements in accuracy. Lower myelin map values predicted improvement in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.

scholarly journals General Functional Connectivity: shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

2018 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
Megan Cooke ◽  
M. Justin Kim ◽  
Tracy R. Melzer ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Biomarker Discovery ◽  
Resting State Functional Connectivity ◽  
Heritable Variation ◽  
State Data ◽  
Human Connectome Project ◽  
Intrinsic Connectivity ◽  
And Task

AbstractIntrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

scholarly journals Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing

2017 ◽  
Vol 118 (2) ◽  
pp. 1235-1243 ◽  
Author(s):  
Heather R. McGregor ◽  
Paul L. Gribble
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Motor Learning ◽  
Resting State ◽  
Brain Function ◽  
Motor Recovery ◽  
Resting State Functional Connectivity ◽  
Brain Areas ◽  
Motor Network ◽  
Sensory Motor

We show that individual differences in preobservation brain function can predict subsequent observation-related gains in motor learning. Preobservation resting-state functional connectivity within a sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if observation is to be used as a way to boost neuroplasticity and sensory-motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke.

scholarly journals Functional Connectivity Between Somatosensory and Motor Brain Areas Predicts Individual Differences in Motor Learning by Observing

2017 ◽  
Author(s):  
Heather R. McGregor ◽  
Paul L. Gribble
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Motor Learning ◽  
Resting State ◽  
Brain Function ◽  
Grey Matter ◽  
Grey Matter Volume ◽  
Resting State Functional Connectivity ◽  
Matter Volume ◽  
Sensory Motor

AbstractAction observation can facilitate the acquisition of novel motor skills, however, there is considerable individual variability in the extent to which observation promotes motor learning. Here we tested the hypothesis that individual differences in brain function or structure can predict subsequent observation-related gains in motor learning. Subjects underwent an anatomical MRI scan and resting-state fMRI scans to assess pre-observation grey matter volume and pre-observation resting-state functional connectivity (FC), respectively. On the following day, subjects observed a video of a tutor adapting her reaches to a novel force field. After observation, subjects performed reaches in a force field as a behavioral assessment of gains in motor learning resulting from observation. We found that individual differences in resting-state FC, but not grey matter volume, predicted post-observation gains in motor learning. Pre-observation resting-state FC between left S1 and bilateral PMd, M1, S1 and left SPL was positively correlated with behavioral measures of post-observation motor learning. Sensory-motor resting-state FC can thus predict the extent to which observation will promote subsequent motor learning.New & NoteworthyWe show that individual differences in pre-observation brain function can predict subsequent observation-related gains in motor learning. Pre-observation resting-state functional connectivity within a sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if observation is to be used as a way to boost neuroplasticity and sensory motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke.

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.

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