Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition

NeuroImage ◽  
2016 ◽  
Vol 124 ◽  
pp. 287-299 ◽  
Author(s):  
Paola Fuentes-Claramonte ◽  
César Ávila ◽  
Aina Rodríguez-Pujadas ◽  
Víctor Costumero ◽  
Noelia Ventura-Campos ◽  
...  
Keyword(s):  
Individual Differences ◽  
Response Inhibition ◽  
Brain Networks ◽  
Reward Sensitivity ◽  
The Brain

scholarly journals Uncontrolled Eating in Healthy Women Has Limited Influence on Food Reward Sensitivity and Food-Related Inhibitory Control

2020 ◽  
Author(s):  
Maud Grol ◽  
Luis Cásedas ◽  
Danna Oomen ◽  
Desirée Spronk ◽  
Elaine Fox
Keyword(s):  
Individual Differences ◽  
Food Consumption ◽  
Inhibitory Control ◽  
Response Inhibition ◽  
Food Reward ◽  
Reward Sensitivity ◽  
Specific Response ◽  
High Tendency ◽  
Healthy Women ◽  
Uncontrolled Eating

Uncontrolled eating—in the general population—is characterized by overeating, hedonic hunger and being drawn towards palatable foods. Theoretically, it is the result of a strong food reward signal in relation to a poor ability to exert inhibitory control. How food consumption influences inhibitory control and food reward sensitivity, and how this relates to the continued urge to eat, remains unclear though. We used fMRI (N=40) in order to investigate the neural mechanism underlying food reward sensitivity and food-specific response inhibition (go-nogo task), by comparing women reporting high versus low/average uncontrolled eating across two sessions: during an inter-meal hunger state and after consumption of a high-caloric snack. We found no effects of individual differences in uncontrolled eating, food consumption, nor their interaction on food reward sensitivity. Differences in uncontrolled eating and food consumption did interact in modulating activity in the left superior occipital gyrus during response inhibition of non-food stimuli, an area previously associated with successful nogo- vs. go-trials. Yet, behavioural performance on the go-nogo task was not modulated by uncontrolled eating nor food consumption. Women with a low/average tendency for uncontrolled eating may need more cognitive resources to support successful response inhibition of non-food stimuli during food ‘go’ blocks in an inter-meal hunger state, whereas women with a high tendency for uncontrolled eating showed this after food consumption. Considering current and previous findings, it seems that individual differences in uncontrolled eating in healthy women have only limited influence on food reward sensitivity and food-related inhibitory control, whereas differences in weight status (e.g., obesity) may have more impact.

How the Brain Makes Potential

2017 ◽  
pp. 168-198
Author(s):  
Ken Richardson
Keyword(s):  
Neural Networks ◽  
Individual Differences ◽  
Brain Networks ◽  
Neural System ◽  
Dynamical Processes ◽  
Brain Functions ◽  
Unpredictable Environments ◽  
The Brain

Chapter 6 describes how a “neural” system of intelligence emerged as more changeable environments were encountered. It contrasts the traditional mechanical and computational metaphors of brain functions (largely based on ideological preconceptions) with the emerging concepts of dynamical processes in neural networks. Only the latter can deal with rapidly changing, unpredictable environments. The chapter goes on to critique efforts to relate individual differences in IQ to differences in brain networks using MRI scanning and related methods.

scholarly journals Unravelling the brain networks driving spike-wave discharges in genetic generalized epilepsy-common patterns and individual differences

2018 ◽  
Vol 3 (4) ◽  
pp. 485-494 ◽  
Author(s):  
Silke Klamer ◽  
Thomas Ethofer ◽  
Franziska Torner ◽  
Ashish Kaul Sahib ◽  
Adham Elshahabi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Networks ◽  
Spike Wave Discharges ◽  
Generalized Epilepsy ◽  
The Brain ◽  
Spike Wave

Lessons From the Neural Bases of Speech and Voice

2011 ◽  
Vol 21 (1) ◽  
pp. 5-14
Author(s):  
Christy L. Ludlow
Keyword(s):  
Brain Networks ◽  
Neural Substrates ◽  
Voice Behavior ◽  
Therapeutic Approaches ◽  
Neural Bases ◽  
Sound Foundation ◽  
The Brain ◽  
Normal Speech

The premise of this article is that increased understanding of the brain bases for normal speech and voice behavior will provide a sound foundation for developing therapeutic approaches to establish or re-establish these functions. The neural substrates involved in speech/voice behaviors, the types of muscle patterning for speech and voice, the brain networks involved and their regulation, and how they can be externally modulated for improving function will be addressed.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

Individual Uniqueness in the Neonatal Functional Connectome

2021 ◽  
Author(s):  
Qiushi Wang ◽  
Yuehua Xu ◽  
Tengda Zhao ◽  
Zhilei Xu ◽  
Yong He ◽  
...  
Keyword(s):  
Individual Differences ◽  
Individual Identification ◽  
Imaging Data ◽  
Functional Connectome ◽  
Middle Range ◽  
Human Connectome Project ◽  
The Individual ◽  
And Behavior ◽  
Identification Analysis ◽  
The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

scholarly journals Cortical silent period reflects individual differences in action stopping performance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mario Paci ◽  
Giulio Di Cosmo ◽  
Mauro Gianni Perrucci ◽  
Francesca Ferri ◽  
Marcello Costantini
Keyword(s):  
Individual Differences ◽  
Response Inhibition ◽  
Primary Motor Cortex ◽  
Silent Period ◽  
Intracortical Inhibition ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Behavioral Differences ◽  
Cortical Silent Period ◽  
Stop Signal Reaction Time

AbstractInhibitory control is the ability to suppress inappropriate movements and unwanted actions, allowing to regulate impulses and responses. This ability can be measured via the Stop Signal Task, which provides a temporal index of response inhibition, namely the stop signal reaction time (SSRT). At the neural level, Transcranial Magnetic Stimulation (TMS) allows to investigate motor inhibition within the primary motor cortex (M1), such as the cortical silent period (CSP) which is an index of GABAB-mediated intracortical inhibition within M1. Although there is strong evidence that intracortical inhibition varies during action stopping, it is still not clear whether differences in the neurophysiological markers of intracortical inhibition contribute to behavioral differences in actual inhibitory capacities. Hence, here we explored the relationship between intracortical inhibition within M1 and behavioral response inhibition. GABABergic-mediated inhibition in M1 was determined by the duration of CSP, while behavioral inhibition was assessed by the SSRT. We found a significant positive correlation between CSP’s duration and SSRT, namely that individuals with greater levels of GABABergic-mediated inhibition seem to perform overall worse in inhibiting behavioral responses. These results support the assumption that individual differences in intracortical inhibition are mirrored by individual differences in action stopping abilities.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders

2020 ◽  
Vol 31 (1) ◽  
pp. 681-693 ◽  
Author(s):  
Emmanuel Peng Kiat Pua ◽  
Phoebe Thomson ◽  
Joseph Yuan-Mou Yang ◽  
Jeffrey M Craig ◽  
Gareth Ball ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Individual Differences ◽  
Face Perception ◽  
Symptom Severity ◽  
Brain Networks ◽  
Monozygotic Twins ◽  
Autism Spectrum ◽  
Social Impairment ◽  
Individual Level ◽  
Spectrum Disorders

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.

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