scholarly journals The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions

GigaScience ◽  
2017 ◽  
Vol 6 (2) ◽  
Author(s):  
David O’Connor ◽  
Natan Vega Potler ◽  
Meagan Kovacs ◽  
Ting Xu ◽  
Lei Ai ◽  
...  
Keyword(s):  
Individual Differences ◽  
Brain Network ◽  
Serial Scanning

scholarly journals Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

2020 ◽  
Author(s):  
Uzma Nawaz ◽  
Ivy Lee ◽  
Adam Beermann ◽  
Shaun Eack ◽  
Matcheri Keshavan ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Negative Symptom ◽  
Individual Variation ◽  
Symptom Severity ◽  
Spatial Organization ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Organization ◽  
Functional Brain Network

Abstract Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P < .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).

Protocols for The Healthy Brain Network Serial Scanning Initiative (Sessions 1-14) v1

protocols.io ◽  
2017 ◽  
Author(s):  
David O ◽  
Natan Vega ◽  
Meagan Kovacs ◽  
Ting Xu ◽  
Lei Ai ◽  
...  
Keyword(s):  
Brain Network ◽  
Serial Scanning

scholarly journals Variability in Infants' Functional Brain Network Connectivity Is Associated With Differences in Affect and Behavior

2021 ◽  
Vol 12 ◽  
Author(s):  
Caroline M. Kelsey ◽  
Katrina Farris ◽  
Tobias Grossmann
Keyword(s):  
Individual Differences ◽  
Long Range ◽  
Network Connectivity ◽  
Brain Network ◽  
Functional Brain ◽  
Default Mode ◽  
Behavioral Traits ◽  
Functional Brain Network ◽  
Brain Network Connectivity ◽  
And Behavior

Variability in functional brain network connectivity has been linked to individual differences in cognitive, affective, and behavioral traits in adults. However, little is known about the developmental origins of such brain-behavior correlations. The current study examined functional brain network connectivity and its link to behavioral temperament in typically developing newborn and 1-month-old infants (M [age] = 25 days; N = 75) using functional near-infrared spectroscopy (fNIRS). Specifically, we measured long-range connectivity between cortical regions approximating fronto-parietal, default mode, and homologous-interhemispheric networks. Our results show that connectivity in these functional brain networks varies across infants and maps onto individual differences in behavioral temperament. Specifically, connectivity in the fronto-parietal network was positively associated with regulation and orienting behaviors, whereas connectivity in the default mode network showed the opposite effect on these behaviors. Our analysis also revealed a significant positive association between the homologous-interhemispheric network and infants' negative affect. The current results suggest that variability in long-range intra-hemispheric and cross-hemispheric functional connectivity between frontal, parietal, and temporal cortex is associated with individual differences in affect and behavior. These findings shed new light on the brain origins of individual differences in early-emerging behavioral traits and thus represent a viable novel approach for investigating developmental trajectories in typical and atypical neurodevelopment.

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 Brain network topology and personality traits: a source level magnetoencephalographic study

2021 ◽  
Author(s):  
Emahnuel Troisi Lopez ◽  
Valentina Colonnello ◽  
Marianna Liparoti ◽  
Mauro Castaldi ◽  
Paolo Maria Russo ◽  
...  
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Caudate Nucleus ◽  
Large Scale ◽  
Brain Network ◽  
Neural Communication ◽  
Character Inventory ◽  
Left Caudate ◽  
Source Level ◽  
Large Scale Networks

Abstract Personality neuroscience is focusing on the correlation between individual differences and the efficiency of large-scale networks from the perspective of the brain as an interconnected network. A suitable technique to explore this relationship is the magnetoencephalography (MEG), but little are MEG studies aimed at investigating topological properties correlated to personality traits. By using MEG, the present study is aimed at evaluating how individual differences described in Cloninger’s psychobiological model are correlated with specific cerebral structures. Fifty healthy individuals (20 males, 30 females, mean age: 27.4 ± 4.8 years) underwent Temperament and Character Inventory examination and MEG recording during a resting state condition. High harm avoidance scores were associated with a reduced centrality of the left caudate nucleus and this negative correlation was maintained in females when we analyzed gender differences. Our data suggest that the caudate nucleus plays a key role in adaptive behavior and could be a critical node in insular salience network. The clear difference between males and females allows us to suggest that topological organization correlated to personality is highly dependent on gender. Our findings provide new insights to evaluate the mutual influences of topological and functional connectivity in neural communication efficiency and disruption as biomarkers of psychopathological traits.

scholarly journals Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure

NeuroImage ◽  
2020 ◽  
Vol 220 ◽  
pp. 116611 ◽  
Author(s):  
Dragana M. Pavlović ◽  
Bryan R.L. Guillaume ◽  
Emma K. Towlson ◽  
Nicole M.Y. Kuek ◽  
Soroosh Afyouni ◽  
...  
Keyword(s):  
Individual Differences ◽  
Human Brain ◽  
Cluster Structure ◽  
Brain Network ◽  
Network Cluster ◽  
Adaptive Analysis

Emotional memory

2019 ◽  
pp. 170-187
Author(s):  
Mana R Ehlers ◽  
Rebecca M Todd
Keyword(s):  
Individual Differences ◽  
Life Experience ◽  
Association Studies ◽  
Gene Clusters ◽  
Emotional Memory ◽  
Brain Network ◽  
Brain And Behavior ◽  
Behavior Function ◽  
Emotional Memory Enhancement ◽  
And Behavior

This chapter presents an overview of current conceptualizations of the emotional enhancement of explicit and implicit memory. It postulates that these processes depend largely on arousal-mediated noradrenergic influences on the amygdala, a key hub in a brain network, conveying information about the (emotional) salience of stimuli and events. Selected genetic polymorphisms known to cause individual differences in neurotransmitter systems mediating emotional memory enhancement are described, as well as how these may be used as tools to investigate effects of neuromodulators and hormones on emotional memory and learning. The conclusion drawn is that studies involving candidate gene approaches should be conducted alongside genome-wide association studies (GWAS) in order to improve understanding of how genetic variations and gene clusters affect emotional memory at a molecular level. Combining earlier findings with novel work obtained from genetic studies will help understand how heritability and life experience mediate and aid generate individual differences in brain and behavior function related to emotional memory.

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