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

2020 ◽  
Vol 226 ◽  
pp. 30-37 ◽  
Author(s):  
Hengyi Cao ◽  
Yoonho Chung ◽  
Sarah C. McEwen ◽  
Carrie E. Bearden ◽  
Jean Addington ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
North American ◽  
Resting State ◽  
Brain Networks ◽  
Preliminary Results ◽  
The North

scholarly journals Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency

2016 ◽  
Vol 47 (4) ◽  
pp. 585-596 ◽  
Author(s):  
K. Baek ◽  
L. S. Morris ◽  
P. Kundu ◽  
V. Voon
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Data Driven ◽  
Local Network ◽  
Healthy Controls ◽  
Network Efficiency ◽  
Whole Brain ◽  
Network Properties ◽  
Obese Subjects

BackgroundThe efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED.MethodMulti-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%–25%. We also verified our findings using a separate parcellation, the Harvard–Oxford atlas parcellated into 470 regions.ResultsObese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis.ConclusionsUsing this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

scholarly journals Persistent frustration-induced reconfigurations of brain networks predict individual differences in irritability

2021 ◽  
Author(s):  
Julia Olivia Linke ◽  
Simone P Haller ◽  
Ellie Xu ◽  
Lynn Nguyen ◽  
Amanda Chue ◽  
...  
Keyword(s):  
Resting State ◽  
Network Architecture ◽  
Goal Attainment ◽  
Recovery Period ◽  
Brain Networks ◽  
Brain Network ◽  
Neural Network Architecture ◽  
Unseen Data ◽  
Network Process ◽  
The Brain

Background. Frustration, the response to blocked goal attainment, is a universal affective experience, but how the brain embodies frustration is not known. Understanding brain network dynamics during frustration may provide insight into pediatric irritability, one of the most frequent reasons for psychiatric consultation in youth and a risk factor for affective disorders and suicidality. Methods. Using fMRI, we investigated changes in neural network architecture from a baseline resting-state, through a task that included frustrative nonreward (FNR) and anticipation of new feedback following FNR (FNR+1), to a post-task resting-state in a transdiagnostic sample of 66 youth (33 female, mean age 14 years). Using a train/test/held-out procedure, we aimed to predict past-week irritability from the global efficiency (i.e., Eglob, capacity for parallel information processing) of brain networks before, during, and after frustration. Results. Compared to pre-task resting state, FNR+1 and the post-state resting state were uniquely associated with a more segregated brain network organization. Nodes that were originally affiliated with the default-mode-temporal-limbic and fronto-parietal networks contributed most to this reconfiguration. Solely Eglob of brain networks that emerged after the frustrating task predicted self- and observer-rated irritability in previously unseen data. Self-reported irritability was predicted by Eglob of a fronto-temporal-limbic module, while observer-rated irritability was predicted by Eglob of motor-parietal and ventral-prefrontal-subcortical modules. Discussion. We characterize frustration as an evolving brain network process and demonstrate the importance of the post-frustration recovery period for the pathophysiology of irritability; an insight that, if replicated, suggests specific intervention targets for irritability.

Visual cortical plasticity and the risk for psychosis: An interim analysis of the North American Prodrome Longitudinal Study

2021 ◽  
Vol 230 ◽  
pp. 26-37
Author(s):  
Michael S. Jacob ◽  
Brian J. Roach ◽  
Holly K. Hamilton ◽  
Ricardo E. Carrión ◽  
Aysenil Belger ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
North American ◽  
Interim Analysis ◽  
Cortical Plasticity ◽  
The North ◽  
Visual Cortical

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 Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory

2021 ◽  
Vol 11 (1) ◽  
pp. 111
Author(s):  
Farzad V. Farahani ◽  
Magdalena Fafrowicz ◽  
Waldemar Karwowski ◽  
Bartosz Bohaterewicz ◽  
Anna Maria Sobczak ◽  
...  
Keyword(s):  
Resting State ◽  
Network Architecture ◽  
Inferior Frontal Gyrus ◽  
Brain Network ◽  
Time Of Day ◽  
Precentral Gyrus ◽  
Network Organization ◽  
Diurnal Variability ◽  
Sleep Inertia ◽  
Brain Functions

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.

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 Brain Network Modeling Based on Mutual Information and Graph Theory for Predicting the Connection Mechanism in the Progression of Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 300 ◽  
Author(s):  
Shuaizong Si ◽  
Bin Wang ◽  
Xiao Liu ◽  
Chong Yu ◽  
Chao Ding ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Mutual Information ◽  
Brain Networks ◽  
Brain Network ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Anatomical Space ◽  
The Brain

Alzheimer’s disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiments show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

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.

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