Altered global brain network topology as a trait marker in patients with anorexia nervosa

2019 ◽  
Vol 50 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Daniel Geisler ◽  
Viola Borchardt ◽  
Ilka Boehm ◽  
Joseph A. King ◽  
Friederike I. Tam ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Resting State ◽  
Network Architecture ◽  
Brain Network ◽  
Global Network ◽  
Local Network ◽  
Support Vector ◽  
Graph Theoretic ◽  
Trait Marker ◽  
Global Brain

AbstractBackgroundResting state functional magnetic resonance imaging studies have identified functional connectivity patterns associated with acute undernutrition in anorexia nervosa (AN), but few have investigated recovered patients. Thus, a trait connectivity profile characteristic of the disorder remains elusive. Using state-of-the-art graph–theoretic methods in acute AN, the authors previously found abnormal global brain network architecture, possibly driven by local network alterations. To disentangle trait from starvation effects, the present study examines network organization in recovered patients.MethodsGraph–theoretic metrics were used to assess resting-state network properties in a large sample of female patients recovered from AN (recAN, n = 55) compared with pairwise age-matched healthy controls (HC, n = 55).ResultsIndicative of an altered global network structure, recAN showed increased assortativity and reduced global clustering as well as small-worldness compared with HC, while no group differences at an intermediate or local network level were evident. However, using support-vector classifier on local metrics, recAN and HC could be separated with an accuracy of 70.4%.ConclusionsThis pattern of results suggests that long-term recovered patients have an aberrant global brain network configuration, similar to acutely underweight patients. While the finding of increased assortativity may represent a trait marker of AN, the remaining findings could be seen as a scar following prolonged undernutrition.

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 Structural brain network topology underpinning ADHD and response to methylphenidate treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kristi R. Griffiths ◽  
Taylor A. Braund ◽  
Michael R. Kohn ◽  
Simon Clarke ◽  
Leanne M. Williams ◽  
...  
Keyword(s):  
Brain Network ◽  
Global Network ◽  
Support Vector ◽  
Structural Topology ◽  
Local Efficiency ◽  
Structural Brain Network ◽  
Structural Architecture ◽  
The Right ◽  
Tractography Data ◽  
Treatment Prognosis

AbstractBehavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

scholarly journals Binocular integrated visual field deficits are associated with changes in local network function in primary open-angle glaucoma: a resting-state fMRI study

2021 ◽  
Author(s):  
Giorgia Demaria ◽  
Azzurra Invernizzi ◽  
Daniel Ombelet ◽  
Joana Carvalho ◽  
Remco Renken ◽  
...  
Keyword(s):  
Visual Field ◽  
Functional Connectivity ◽  
Resting State ◽  
Primary Open Angle Glaucoma ◽  
Open Angle Glaucoma ◽  
Brain Regions ◽  
Local Network ◽  
Open Angle ◽  
Significant Difference ◽  
Global Brain

Recent brain imaging studies have shown that the degenerative eye damage generally observed in the clinical setting, also extends intracranially. Both structural and functional brain changes have been observed in glaucoma participants, but we still lack an understanding of whether these changes also affect the integrity of cortical functional networks. This is relevant, as functional network integrity may affect the applicability of future treatments, as well as the options for rehabilitation or training. Here, we compare global and local functional connectivity between glaucoma and controls. Moreover, we study the relationship between functional connectivity and visual field (VF) loss. For our study, 20 subjects with primary open angle glaucoma (POAG) and 24 age similar healthy participants were recruited to undergo a complete ophthalmic assessment followed by two resting state (RS) (f)MRI scans. For each scan and for each group, the ROIs with EC values higher than the 95th percentile were considered the most central brain regions (hubs). Hubs for which we found a significant difference in EC in both scans between glaucoma and healthy were considered to provide evidence for network changes. In addition, for each participant, behavioural scores were derived based on the notion that a brain regions hub function might relate to the: 1) sensitivity of the worse eye, indicating disease severity, 2) sensitivity of both eyes combined, with one eye potentially compensating for loss in the other, or 3) difference in eye sensitivity, requiring additional network interactions. By correlating each of these VF scores and the EC values, we assessed whether VF defects could be associated with centrality alterations in POAG. Our results show that no functional connectivity disruptions were found at the global brain level in POAG participants. This indicates that in glaucoma global brain network communication is preserved. Furthermore, a positive correlation was found between the EC value of the Lingual Gyrus, identified as a brain hub, and the behavioral score for the VF sensitivity of both eyes combined. The fact that reduced local network functioning is associated with reduced binocular VF sensitivity suggests the presence of local brain reorganization that has a bearing on functional visual abilities.

scholarly journals Brain Resting State Functional Networks in Infants with Prenatal Opioid Exposure

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicole Steinhardt ◽  
Ramana Vishnubhotla ◽  
Yi Zhao ◽  
David M. Haas ◽  
Gregory M. Sokol ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Gestational Age ◽  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Global Network ◽  
Neurodevelopmental Outcomes ◽  
Distinct Component

Purpose: Infants of mothers with opioid and substance use can present with postnatal withdrawal symptoms and are at risk of poor neurodevelopmental outcomes in later childhood. Identifying methods to evaluate the consequences of substance exposure on the developing brain can help initiate proactive therapies to improve outcomes for opioid-exposed neonates. Additionally, early brain imaging in infancy has the potential to identify early brain developmental alterations that could prognosticate neurodevelopmental outcomes in these children. In this study, we aim to identify differences in global brain network connectivity in infants with prenatal opioid exposure compared to healthy control infants, using resting-state functional MRI performed at less than 2 months completed gestational age.   Materials and Methods: In this prospective, IRB-approved study, we recruited 20 infants with prenatal opioid exposure and 20 healthy, opioid naïve infants. Anatomic imaging and resting-state functional MRI were performed at less than 48 weeks corrected gestational age, and rs-fMRI images were co-registered to the UNC neonate brain template and 90 anatomic atlas-labelled regions. Covariate Assisted Principal (CAP) regression was performed to identify brain network functional connectivity that was significantly different among infants with prenatal opioid exposure compared to healthy neonates.   Results: Of the 5 significantly different CAP components identified, the most distinct component (CAP5, p= 3.86 x 10-6) spanned several brain regions, including the right inferior temporal gyrus, bilateral Hesch’s gyrus, left thalamus, left supramarginal gyrus, left inferior parietal lobule, left superior parietal gyrus, right anterior cingulate gyrus, right gyrus rectus, left supplementary motor area, and left pars triangularis. Functional connectivity in this network was lower in the infants with prenatal opioid exposure compared to non-opioid exposed infants.   Conclusion: This study demonstrates global network alterations in infants with prenatal opioid exposure compared to non-opioid exposed infants. Future studies should be aimed at identifying clinical significance of this altered connectivity.

scholarly journals Research on Distributed Data Sharing System based on Internet of Things and Blockchain

2021 ◽  
Vol 9 (3) ◽  
pp. 239-254
Author(s):  
Enchang Sun ◽  
Kang Meng ◽  
Ruizhe Yang ◽  
Yanhua Zhang ◽  
Meng Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Data Storage ◽  
Network Architecture ◽  
Data Privacy ◽  
Distributed Storage ◽  
Global Network ◽  
Local Network ◽  
Network Data ◽  
Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

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 Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning

2021 ◽  
Vol 13 ◽  
Author(s):  
Zhaoshun Jiang ◽  
Yuxi Cai ◽  
Xixue Zhang ◽  
Yating Lv ◽  
Mengting Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Cardiac Surgery ◽  
Prefrontal Cortex ◽  
Random Forest ◽  
Resting State ◽  
Brain Network ◽  
Support Vector ◽  
Whole Brain ◽  
The Right

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR.Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).

scholarly journals Latent functional connectivity underlying multiple brain states

2021 ◽  
Author(s):  
Ethan M McCormick ◽  
Katelyn L Arnemann ◽  
Takuya Ito ◽  
Stephen Jose Hanson ◽  
Michael W Cole
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Architecture ◽  
Brain Activity ◽  
Brain Network ◽  
Mathematical Framework ◽  
Human Connectome Project ◽  
Brain States ◽  
Task Conditions ◽  
Psychometric G

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to primarily reflect the brain's intrinsic network architecture, which is thought to be broadly relevant to brain function because it persists across brain states. However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting patterns of connectivity shared across many brain states, may better capture intrinsic FC relative to measures derived from resting state alone. We estimated latent FC in relation to 7 highly distinct task states (24 task conditions) and resting state using fMRI data from 352 participants from the Human Connectome Project. Latent FC was estimated independently for each connection by applying leave-one-task-out factor analysis on the state FC estimates. Compared to resting-state connectivity, we found that latent connectivity improves generalization to held-out brain states, better explaining patterns of both connectivity and task-evoked brain activity. We also found that latent connectivity improved prediction of behavior, measured by the general intelligence factor psychometric g. Our results suggest that patterns of FC shared across many brain states, rather than just resting state, better reflects general, state-independent connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

scholarly journals Bridging global and local topology in whole-brain networks using the network statistic jackknife

2020 ◽  
Vol 4 (1) ◽  
pp. 70-88 ◽  
Author(s):  
Teague R. Henry ◽  
Kelly A. Duffy ◽  
Marc D. Rudolph ◽  
Mary Beth Nebel ◽  
Stewart H. Mostofsky ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Structure ◽  
R Package ◽  
Brain Network ◽  
Global Network ◽  
Local Network ◽  
Network Organization ◽  
Whole Brain ◽  
Global Efficiency ◽  
Global Topology

Whole-brain network analysis is commonly used to investigate the topology of the brain using a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network organization relates to cognition and behavior and examining disrupted brain network organization in disease. A benefit to this approach is the ability to summarize overall brain network organization with a single metric (e.g., global efficiency). However, important local differences in network structure might exist without any corresponding observable differences in global topology, making a whole-brain analysis strategy unlikely to detect relevant local findings. Conversely, using local network metrics can identify local differences, but are not directly informative of differences in global topology. Here, we propose the network statistic (NS) jackknife framework, a simulated lesioning method that combines the utility of global network analysis strategies with the ability to detect relevant local differences in network structure. We evaluate the NS jackknife framework with a simulation study and an empirical example comparing global efficiency in children with attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) children. The NS jackknife framework has been implemented in a public, open-source R package, netjack, available at https://cran.r-project.org/package=netjack .

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