Individual differences in temperament and the efficiency of brain networks

2022 ◽  
Vol 43 ◽  
pp. 242-248
Author(s):  
Mary K Rothbart ◽  
Michael I Posner
2020 ◽  
Vol 31 (1) ◽  
pp. 681-693 ◽  
Author(s):  
Emmanuel Peng Kiat Pua ◽  
Phoebe Thomson ◽  
Joseph Yuan-Mou Yang ◽  
Jeffrey M Craig ◽  
Gareth Ball ◽  
...  

Abstract The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case–control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.


2019 ◽  
Vol 40 ◽  
pp. 100706 ◽  
Author(s):  
Scott Marek ◽  
Brenden Tervo-Clemmens ◽  
Ashley N. Nielsen ◽  
Muriah D. Wheelock ◽  
Ryland L. Miller ◽  
...  

2019 ◽  
Vol 30 (3) ◽  
pp. 1087-1102
Author(s):  
Shi Gu ◽  
Cedric Huchuan Xia ◽  
Rastko Ciric ◽  
Tyler M Moore ◽  
Ruben C Gur ◽  
...  

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242985
Author(s):  
Howard Muchen Hsu ◽  
Zai-Fu Yao ◽  
Kai Hwang ◽  
Shulan Hsieh

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.


2020 ◽  
Author(s):  
Youngheun Jo ◽  
Farnaz Zamani Esfahlani ◽  
Joshua Faskowitz ◽  
Evgeny J. Chumin ◽  
Olaf Sporns ◽  
...  

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions’ “edge community profiles”, we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions’ edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brain’s system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.


2020 ◽  
Vol 142 ◽  
pp. 107426
Author(s):  
Yu Mao ◽  
Ryota Kanai ◽  
Cody Ding ◽  
Taiyong Bi ◽  
Jiang Qiu

2014 ◽  
Vol 112 (8) ◽  
pp. 1838-1848 ◽  
Author(s):  
Kelly Anne Barnes ◽  
Kevin M. Anderson ◽  
Mark Plitt ◽  
Alex Martin

When humans are provided with ample time to make a decision, individual differences in strategy emerge. Using an adaptation of a well-studied decision making paradigm, motion direction discrimination, we probed the neural basis of individual differences in strategy. We tested whether strategies emerged from moment-to-moment reconfiguration of functional brain networks involved in decision making with task-evoked functional MRI (fMRI) and whether intrinsic properties of functional brain networks, measured at rest with functional connectivity MRI (fcMRI), were associated with strategy use. We found that human participants reliably selected one of two strategies across 2 days of task performance, either continuously accumulating evidence or waiting for task difficulty to decrease. Individual differences in decision strategy were predicted both by the degree of task-evoked activation of decision-related brain regions and by the strength of pretask correlated spontaneous brain activity. These results suggest that spontaneous brain activity constrains strategy selection on perceptual decisions.


2019 ◽  
Author(s):  
Ashvanti Valji ◽  
Alisa Priemysheva ◽  
Carl J. Hodgetts ◽  
Alison G. Costigan ◽  
Greg D. Parker ◽  
...  

AbstractAcross the lifespan, curiosity motivates us to learn, yet curiosity varies strikingly between individuals. Such individual differences have been shown for two distinct dimensions of curiosity: epistemic curiosity (EC), the desire to acquire conceptual knowledge, and perceptual curiosity (PC), the desire for sensory information. It is not known, however, whether both dimensions of curiosity depend on different brain networks and whether inter-individual differences in curiosity depend on variation in anatomical connectivity within these networks. Here, we investigated the neuroanatomical connections underpinning individual variation in trait curiosity. Fifty-one female participants underwent a two-shell diffusion MRI sequence and completed questionnaires measuring EC and PC. Using deterministic spherical deconvolution tractography we extracted microstructural metrics (fractional anisotropy (FA) and mean diffusivity (MD)) from two key white matter tracts: the fornix (implicated in novelty processing, exploration, information seeking and episodic memory) and the inferior longitudinal fasciculus (ILF) (implicated in semantic learning and memory). In line with our predictions, we found that EC – but not PC – correlated with ILF microstructure. Fornix microstructure, in contrast, correlated with both EC and PC, with posterior hippocampal fornix fibres - associated with posterior hippocampal network connectivity - linked to PC specifically. These findings suggest that differences in distinct dimensions of curiosity map systematically onto specific white matter tracts underlying well characterized brain networks. Furthermore, the results pave the way to study the anatomical substrates of inter-individual differences in dimensions of trait curiosity that motivate the learning of distinct forms of knowledge and skills.


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