scholarly journals General Anesthesia and Human Brain Connectivity

2012 ◽  
Vol 2 (6) ◽  
pp. 291-302 ◽  
Author(s):  
Anthony G. Hudetz
Keyword(s):  
Human Brain ◽  
General Anesthesia ◽  
Brain Connectivity

scholarly journals Metabolomic Profiling of Children’s Brains Undergoing General Anesthesia with Sevoflurane and Propofol

2012 ◽  
Vol 117 (5) ◽  
pp. 1062-1071 ◽  
Author(s):  
Zvi Jacob ◽  
Haifang Li ◽  
Rany Makaryus ◽  
Shaonan Zhang ◽  
Ruth Reinsel ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
General Anesthesia ◽  
Brain Connectivity ◽  
Pediatric Anesthesia ◽  
Metabolic Status ◽  
Resonance Spectroscopy ◽  
Emergence Delirium ◽  
Metabolomic Profiling ◽  
Rodent Brain

Background We recently applied proton magnetic resonance spectroscopy (HMRS) to investigate metabolic consequences of general anesthesia in the rodent brain, and discovered that isoflurane anesthesia was characterized by higher concentrations of lactate, glutamate, and glucose in comparison with propofol. We hypothesized that the metabolomic differences between an inhalant and intravenous anesthetic observed in the rodent brain could be reproduced in the human brain. Methods HMRS-based metabolomic profiling was applied to characterize the cerebral metabolic status of 59 children undergoing magnetic resonance imaging during anesthesia with either sevoflurane or propofol. HMRS scans were acquired in the parietal cortex after approximately 60 min of anesthesia. Upon emergence the children were assessed using the pediatric anesthesia emergence delirium scale. Results With sevoflurane anesthesia, the metabolic signature consisted of higher concentrations of lactate and glucose compared with children anesthetized with propofol. Further, a correlation and stepwise regression analysis performed on emergence delirium scores in relation to the metabolic status revealed that lactate and glucose correlated positively and total creatine negatively with the emergence delirium score. Conclusions Our results demonstrating higher glucose and lactate with sevoflurane in the human brain compared with propofol could reflect greater neuronal activity with sevofluane resulting in enhanced glutamate-neurotransmitter cycling, increased glycolysis, and lactate shuttling from astrocytes to neurons or mitochondrial dysfunction. Further, the association between emergence delirium and lactate suggests that anesthesia-induced enhanced cortical activity in the unconscious state may interfere with rapid return to "coherent" brain connectivity patterns required for normal cognition upon emergence of anesthesia.

scholarly journals Diffusion tensor-based fast marching for modeling human brain connectivity network

2011 ◽  
Vol 35 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Hai Li ◽  
Zhong Xue ◽  
Kemi Cui ◽  
Stephen T.C. Wong
Keyword(s):  
Human Brain ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Fast Marching

scholarly journals Evolutionary modifications in human brain connectivity associated with schizophrenia

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3991-4002 ◽  
Author(s):  
Martijn P van den Heuvel ◽  
Lianne H Scholtens ◽  
Siemon C de Lange ◽  
Rory Pijnenburg ◽  
Wiepke Cahn ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Brain Evolution ◽  
Higher Order ◽  
Brain Functions ◽  
Human Brain Evolution ◽  
The Brain

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.

scholarly journals Resources available for autism research in the big data era: a systematic review

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2880 ◽  
Author(s):  
Reem Al-jawahiri ◽  
Elizabeth Milne
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Open Data ◽  
Autism Spectrum ◽  
Data Types ◽  
Participant Recruitment ◽  
Data Resource ◽  
Autism Research ◽  
Great Heterogeneity ◽  
Google Search

Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD). The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research. In order to provide a useful data reference for autism researchers, a systematic search for ASD data resources was conducted using the Scopus database, the Google search engine, and the pages on ‘recommended repositories’ by key journals, and the findings were translated into a comprehensive list focused on ASD data. The aim of this review is to systematically search for all available ASD data resources providing the following data types: phenotypic, neuroimaging, human brain connectivity matrices, human brain statistical maps, biospecimens, and ASD participant recruitment. A total of 33 resources were found containing different types of data from varying numbers of participants. Description of the data available from each data resource, and links to each resource is provided. Moreover, key implications are addressed and underrepresented areas of data are identified.

scholarly journals Towards a natural history of schizophrenia

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3669-3671
Author(s):  
Petra E Vértes ◽  
Jakob Seidlitz
Keyword(s):  
Natural History ◽  
Human Brain ◽  
Brain Connectivity ◽  
History Of

This scientific commentary refers to ‘Evolutionary modifications in human brain connectivity associated with schizophrenia’ by van den Heuvel et al. (doi:10.1093/brain/awz330).

scholarly journals Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 128 ◽  
Author(s):  
Aline Viol ◽  
Fernanda Palhano-Fontes ◽  
Heloisa Onias ◽  
Draulio de Araujo ◽  
Philipp Hövel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Structural Information ◽  
Brain Connectivity ◽  
Functional Brain ◽  
States Of Consciousness ◽  
Functional Brain Networks ◽  
Empirical Networks ◽  
Emergent Behaviors

With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

scholarly journals Prevalence of white matter pathways coming into a single diffusion MRI voxel orientation: the bottleneck issue in tractography

2021 ◽  
Author(s):  
Kurt Schilling ◽  
Chantal M.W. Tax ◽  
Francois M.W. Rheault ◽  
Bennett A Landman ◽  
Adam W Anderson ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
False Negative ◽  
Fiber Tractography ◽  
Single Voxel ◽  
Ill Posed ◽  
Pass Through

Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub-cortical terminations. These so-called "bottleneck" regions contribute to the ill-posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50-70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered "single fiber voxels" often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

scholarly journals Spatial Separability in Hub Location Problems with an Application to Brain Connectivity Networks

2020 ◽  
Vol 2 (4) ◽  
pp. 320-346 ◽  
Author(s):  
Taghi Khaniyev ◽  
Samir Elhedhli ◽  
Fatih Safa Erenay
Keyword(s):  
Human Brain ◽  
Medical Literature ◽  
Brain Connectivity ◽  
Three Dimensional ◽  
Location Problems ◽  
Hub Location ◽  
Location Models ◽  
Wide Range ◽  
Data Driven Approach ◽  
Spatial Separability

Motivated by the need to solve large hub location problems efficiently and accurately, we discover an important characteristic of optimal solutions to p-hub median problems that we call spatial separability. It refers to the partitioning of the network into allocation clusters with nonoverlapping convex hulls. We illustrate numerically that the property persists over a wide range of randomly generated instances and propose a data-driven approach based on an insight from the property to tackle very large problem sizes. Computational experiments corroborate the effectiveness of the proposed approach in generating high-quality solutions within reasonable computational times. We then explore a new application area of hub location problems in brain connectivity networks and introduce the largest and the first set of three-dimensional instances in the literature. Computational results demonstrate the capability of hub location models in successfully depicting the hub organization of the human brain, as validated by the medical literature, thus revealing that hub location models can play an important role in investigating the intricate connectivity of the human brain.

scholarly journals Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification

2019 ◽  
Vol 33 ◽  
pp. 1198-1205 ◽  
Author(s):  
Mingliang Wang ◽  
Jiashuang Huang ◽  
Mingxia Liu ◽  
Daoqiang Zhang
Keyword(s):  
Network Analysis ◽  
Human Brain ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Disease Diagnosis ◽  
Brain Network ◽  
Brain Disease ◽  
Brain Diseases ◽  
Central Position ◽  
Connectivity Patterns

Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position in the overall organization of a network), providing essential clues to describe connectivity patterns. However, existing studies often fail to explore such hubs from brain connectivity networks. To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data. Specifically, we incorporate both feature extraction of brain networks and network-based classification into a unified model, while discriminative hubs can be automatically identified from data via ℓ1-norm and ℓ2,1-norm regularizers. The proposed CNHD method is evaluated on three real-world schizophrenia datasets with fMRI scans. Experimental results demonstrate that our method not only outperforms several state-of-the-art approaches in disease diagnosis, but also is effective in automatically identifying disease-related network hubs in the human brain.

scholarly journals Characterizing Brain Connectivity From Human Electrocorticography Recordings With Unobserved Inputs During Epileptic Seizures

2019 ◽  
Vol 31 (7) ◽  
pp. 1271-1326 ◽  
Author(s):  
Anup Das ◽  
Daniel Sexton ◽  
Claudia Lainscsek ◽  
Sydney S. Cash ◽  
Terrence J. Sejnowski
Keyword(s):  
Human Brain ◽  
Latent Variables ◽  
Brain Research ◽  
Brain Connectivity ◽  
Correlation Method ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Eigenvector Centrality ◽  
Common Input

Epilepsy is a neurological disorder characterized by the sudden occurrence of unprovoked seizures. There is extensive evidence of significantly altered brain connectivity during seizure periods in the human brain. Research on analyzing human brain functional connectivity during epileptic seizures has been limited predominantly to the use of the correlation method. However, spurious connectivity can be measured between two brain regions without having direct connection or interaction between them. Correlations can be due to the apparent interactions of the two brain regions resulting from common input from a third region, which may or may not be observed. Hence, researchers have recently proposed a sparse-plus-latent-regularized precision matrix (SLRPM) when there are unobserved or latent regions interacting with the observed regions. The SLRPM method yields partial correlations of the conditional statistics of the observed regions given the latent regions, thus identifying observed regions that are conditionally independent of both the observed and latent regions. We evaluate the performance of the methods using a spring-mass artificial network and assuming that some nodes cannot be observed, thus constituting the latent variables in the example. Several cases have been considered, including both sparse and dense connections, short-range and long-range connections, and a varying number of latent variables. The SLRPM method is then applied to estimate brain connectivity during epileptic seizures from human ECoG recordings. Seventy-four clinical seizures from five patients, all having complex partial epilepsy, were analyzed using SLRPM, and brain connectivity was quantified using modularity index, clustering coefficient, and eigenvector centrality. Furthermore, using a measure of latent inputs estimated by the SLRPM method, it was possible to automatically detect 72 of the 74 seizures with four false positives and find six seizures that were not marked manually.

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