scholarly journals Modular reconfiguration of an auditory control brain network supports adaptive listening behavior

2018 ◽  
Vol 116 (2) ◽  
pp. 660-669 ◽  
Author(s):  
Mohsen Alavash ◽  
Sarah Tune ◽  
Jonas Obleser
Keyword(s):  
Spatial Attention ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Control Network ◽  
Speech Comprehension ◽  
Individual Level ◽  
Auditory Spatial Attention ◽  
The Individual ◽  
Listening Task

Speech comprehension in noisy, multitalker situations poses a challenge. Successful behavioral adaptation to a listening challenge often requires stronger engagement of auditory spatial attention and context-dependent semantic predictions. Human listeners differ substantially in the degree to which they adapt behaviorally and can listen successfully under such circumstances. How cortical networks embody this adaptation, particularly at the individual level, is currently unknown. We here explain this adaptation from reconfiguration of brain networks for a challenging listening task (i.e., a linguistic variant of the Posner paradigm with concurrent speech) in an age-varying sample of n = 49 healthy adults undergoing resting-state and task fMRI. We here provide evidence for the hypothesis that more successful listeners exhibit stronger task-specific reconfiguration (hence, better adaptation) of brain networks. From rest to task, brain networks become reconfigured toward more localized cortical processing characterized by higher topological segregation. This reconfiguration is dominated by the functional division of an auditory and a cingulo-opercular module and the emergence of a conjoined auditory and ventral attention module along bilateral middle and posterior temporal cortices. Supporting our hypothesis, the degree to which modularity of this frontotemporal auditory control network is increased relative to resting state predicts individuals’ listening success in states of divided and selective attention. Our findings elucidate how fine-tuned cortical communication dynamics shape selection and comprehension of speech. Our results highlight modularity of the auditory control network as a key organizational principle in cortical implementation of auditory spatial attention in challenging listening situations.

scholarly journals Modular reconfiguration of an auditory-control brain network supports adaptive listening behavior

2018 ◽  
Author(s):  
Mohsen Alavash ◽  
Sarah Tune ◽  
Jonas Obleser
Keyword(s):  
Spatial Attention ◽  
Speech Processing ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Control Network ◽  
Speech Comprehension ◽  
Auditory Spatial Attention ◽  
Listening Behavior ◽  
Communication Dynamics

AbstractSpeech comprehension in noisy, multi-talker situations poses a challenge. Human listeners differ substantially in the degree to which they adapt behaviorally and can listen successfully under such circumstances. How cortical networks embody this adaptation, particularly at the individual level, is currently unknown. We here explain this adaptation from reconfiguration of brain networks for a challenging listening task (i.e., a novel linguistic variant of the Posner paradigm with concurrent speech) in an age-varying sample of N = 49 healthy adults undergoing resting-state and task fMRI. We here provide evidence for the hypothesis that more successful listeners exhibit stronger task-specific reconfiguration, hence better adaptation, of brain networks. From rest to task, brain networks become reconfigured towards more localized cortical processing characterized by higher topological segregation. This reconfiguration is dominated by the functional division of an auditory and a cingulo-opercular module, and the emergence of a conjoined auditory and ventral attention module along bilateral middle and posterior temporal cortices. Supporting our hypothesis, the degree to which modularity of this fronto-temporal auditory-control network is increased relative to resting state predicts individuals’ listening success in states of divided and selective attention. Our findings elucidate how fine-tuned cortical communication dynamics shape selection and comprehension of speech. Our results highlight modularity of the auditory-control network as a key organizational principle in cortical implementation of auditory spatial attention in challenging listening situations.Significance StatementHow do brain networks shape our listening behavior? We here develop and test the hypothesis that, during challenging listening situations, intrinsic brain networks are reconfigured to adapt to the listening demands, and thus to enable successful listening. We find that, relative to a task-free resting state, networks of the listening brain show higher segregation of temporal auditory, ventral attention, and frontal control regions known to be involved in speech processing, sound localization, and effortful listening. Importantly, the relative change in modularity of this auditory-control network predicts individuals’ listening success. Our findings shed light on how cortical communication dynamics tune selection and comprehension of speech in challenging listening situations, and suggest modularity as the network principle of auditory spatial attention.

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.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Lardone ◽  
Marianna Liparoti ◽  
Pierpaolo Sorrentino ◽  
Rosaria Rucco ◽  
Francesca Jacini ◽  
...  
Keyword(s):  
Resting State ◽  
Mindfulness Meditation ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Long Term Effects ◽  
Age Related ◽  
Functional Topology ◽  
The Right ◽  
The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

scholarly journals Discriminating Suicide Attempters and Predicting Suicide Risk Using Altered Frontolimbic Resting-State Functional Connectivity in Patients With Bipolar II Disorder

2020 ◽  
Vol 11 ◽  
Author(s):  
Rongxin Zhu ◽  
Shui Tian ◽  
Huan Wang ◽  
Haiteng Jiang ◽  
Xinyi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Suicide Risk ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Bipolar Ii Disorder ◽  
Individual Level ◽  
Bipolar Ii ◽  
The Individual

Bipolar II disorder (BD-II) major depression episode is highly associated with suicidality, and objective neural biomarkers could be key elements to assist in early prevention and intervention. This study aimed to integrate altered brain functionality in the frontolimbic system and machine learning techniques to classify suicidal BD-II patients and predict suicidality risk at the individual level. A cohort of 169 participants were enrolled, including 43 BD-II depression patients with at least one suicide attempt during a current depressive episode (SA), 62 BD-II depression patients without a history of attempted suicide (NSA), and 64 demographically matched healthy controls (HCs). We compared resting-state functional connectivity (rsFC) in the frontolimbic system among the three groups and explored the correlation between abnormal rsFCs and the level of suicide risk (assessed using the Nurses' Global Assessment of Suicide Risk, NGASR) in SA patients. Then, we applied support vector machines (SVMs) to classify SA vs. NSA in BD-II patients and predicted the risk of suicidality. SA patients showed significantly decreased frontolimbic rsFCs compared to NSA patients. The left amygdala-right middle frontal gyrus (orbital part) rsFC was negatively correlated with NGASR in the SA group, but not the severity of depressive or anxiety symptoms. Using frontolimbic rsFCs as features, the SVMs obtained an overall 84% classification accuracy in distinguishing SA and NSA. A significant correlation was observed between the SVMs-predicted NGASR and clinical assessed NGASR (r = 0.51, p = 0.001). Our results demonstrated that decreased rsFCs in the frontolimbic system might be critical objective features of suicidality in BD-II patients, and could be useful for objective prediction of suicidality risk in individuals.

scholarly journals Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

2020 ◽  
Author(s):  
Pesoli Matteo ◽  
Rucco Rosaria ◽  
Liparoti Marianna ◽  
Lardone Anna ◽  
D’Aurizio Giula ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Functional Networks ◽  
Long Sleep ◽  
Before And After ◽  
The Brain

AbstractThe topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

scholarly journals Individualized Precision Targeting of Dorsal Attention and Default Mode Networks with Rtms in Traumatic Brain Injury-Associated Depression

2021 ◽  
Author(s):  
Shan H. Siddiqi ◽  
Sridhar Kandala ◽  
Carl D. Hacker ◽  
Nicholas T. Trapp ◽  
Eric C. Leuthardt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Brain Network ◽  
Individual Variability ◽  
Anterior Cingulate ◽  
Default Mode ◽  
Individual Level ◽  
Antidepressant Efficacy ◽  
Personalized Approach ◽  
The Individual

Abstract Background At the group level, antidepressant efficacy of rTMS targets is inversely related to their normative connectivity with subgenual anterior cingulate cortex (sgACC). Individualized connectivity may yield better targets, particularly in patients with neuropsychiatric disorders who may have aberrant connectivity. However, sgACC connectivity shows poor test-retest reliability at the individual level. Individualized resting-state network mapping (RSNM) can reliably map inter-individual variability in brain network organization. Objective To identify individualized RSNM-based rTMS targets that reliably target the sgACC connectivity profile. Methods We used RSNM to identify network-based rTMS targets in 10 healthy controls and 13 individuals with traumatic brain injury-associated depression (TBI-D). These “RSNM targets” were compared with consensus structural targets and targets based on individualized anti-correlation with a group-mean-derived sgACC region (“anti-group-mean sgACC targets”). The TBI-D cohort was randomized to receive active (n=9) or sham (n=4) rTMS to RSNM targets. Results The group-mean sgACC connectivity profile was reliably estimated by individualized correlation with default mode network (DMN) and anti-correlation with dorsal attention network (DAN). Individualized RSNM targets were then identified based on DAN anti-correlation and DMN correlation. Counterintuitively, anti-correlation with the group-mean sgACC connectivity profile was stronger and more reliable for RSNM-derived targets than for “anti-group-mean sgACC targets”. Improvement in depression after RSNM-targeted rTMS was predicted by target anti-correlation with the portions of sgACC. Active treatment led to increased connectivity within and between several relevant regions. Conclusions RSNM may enable reliable individualized rTMS targeting, although further research is needed to determine whether this personalized approach can improve clinical outcomes.

scholarly journals Effects of listening task characteristics on auditory spatial attention in multi-source environment

2021 ◽  
Vol 42 (1) ◽  
pp. 12-21
Author(s):  
Ryo Teraoka ◽  
Shuichi Sakamoto ◽  
Zhenglie Cui ◽  
Yôiti Suzuki ◽  
Satoshi Shioiri
Keyword(s):  
Spatial Attention ◽  
Task Characteristics ◽  
Auditory Spatial Attention ◽  
Listening Task

scholarly journals Progressive reconfiguration of resting-state brain networks as psychosis develops: Preliminary results from the North American Prodrome Longitudinal Study (NAPLS) consortium

2017 ◽  
Author(s):  
Hengyi Cao ◽  
Yoonho Chung ◽  
Sarah C. McEwen ◽  
Carrie E. Bearden ◽  
Jean Addington ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
North American ◽  
Resting State ◽  
Network Architecture ◽  
Brain Networks ◽  
Brain Network ◽  
Network Efficiency ◽  
The North ◽  
Network Diversity ◽  
Global Efficiency

AbstractMounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared underlying neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, are implicated in the progression to full psychosis.

scholarly journals Oxytocin modulates social brain network correlations in resting and task state

2022 ◽  
Author(s):  
Qingyuan Wu ◽  
Qi Huang ◽  
Chao Liu ◽  
Haiyan Wu
Keyword(s):  
Resting State ◽  
Predictive Power ◽  
Brain Networks ◽  
Brain Network ◽  
System Level ◽  
Social Brain ◽  
The Social ◽  
The Difference ◽  
The Relationship ◽  
And Task

Oxytocin (OT) is a neuropeptide that modulates social behaviors and the social brain. The effects of OT on the social brain can be tracked by assessing the neural activity in the resting and task states, providing a system-level framework for characterizing state-based functional relationships of its distinct effect. Here, we contribute to this framework by examining how OT modulates social brain network correlations during the resting and task states using fMRI. Firstly, we investigated network activation, followed by analyzing the relationship between networks and individual differences measured by the Positive and Negative Affect Schedule and the Big-Five scales. Subsequently, we evaluated functional connectivity in both states. Finally, the relationship between networks across the states was represented by the predictive power of networks in the resting state for task-evoked activity. The difference in predicted accuracy between subjects displayed individual variations in this relationship. Our results showed decreased dorsal default mode network (DDMN) for OT group in the resting state. Additionally, only in the OT group, the activity of the DDMN in the resting state had the largest predictive power for task-evoked activation of the precuneus network (PN). The results also demonstrated OT reduced individual variation of PN, specifically, the difference of accuracy between predicting a subject's own and others' PN task activation. These findings suggest a distributed but modulatory effect of OT on the association between resting brain networks and task-dependent brain networks, showing increased DDMN to PN connectivity after OT administration, which may support OT-induced distributed processing during task performance.

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