scholarly journals Genetic, Cellular, and Connectomic Characterization of the Adult Human Brain Regions Commonly Plagued by Glioma

2020 ◽  
Author(s):  
Ayan S. Mandal ◽  
Rafael Romero-Garcia ◽  
Michael G. Hart ◽  
John Suckling
Keyword(s):  
Brain Regions ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Frequency Map ◽  
Multiple Graph ◽  
Anatomic Localization ◽  
The Brain

AbstractA better understanding of the nonrandom localization patterns of gliomas across the brain could lend clues to the origins of these types of tumors. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with stem-like cells also exhibited a high glioma frequency. Furthermore, gliomas were localized to brain regions enriched with the expression of genes associated with chromatin organization and synaptic signaling. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of oncogenesis.

scholarly journals Genetic, cellular, and connectomic characterization of the brain regions commonly plagued by glioma

Brain ◽  
2020 ◽  
Vol 143 (11) ◽  
pp. 3294-3307
Author(s):  
Ayan S Mandal ◽  
Rafael Romero-Garcia ◽  
Michael G Hart ◽  
John Suckling
Keyword(s):  
Occipital Lobe ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Brain Atlas ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Frequency Map ◽  
Multiple Graph

Abstract For decades, it has been known that gliomas follow a non-random spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the localization patterns of gliomas could provide clues to the origins of these types of tumours, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumour frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells of origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signalling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.

scholarly journals NIMG-13. GENETIC, CELLULAR, AND CONNECTOMIC CHARACTERIZATION OF THE ADULT HUMAN BRAIN REGIONS COMMONLY PLAGUED BY GLIOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii149-ii149
Author(s):  
Ayan Mandal ◽  
Rafael Romero-Garcia ◽  
Michael Hart ◽  
John Suckling
Keyword(s):  
Human Brain ◽  
Occipital Lobe ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Brain Atlas ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Multiple Graph

Abstract For decades, it has been known that gliomas follow a nonrandom spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the glioma localization patterns could lend clues to the origins of these types of tumors, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrate that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells-of-origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signaling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.

scholarly journals A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

2021 ◽  
Vol 15 ◽  
Author(s):  
Paolo Finotelli ◽  
Carlo Piccardi ◽  
Edie Miglio ◽  
Paolo Dulio
Keyword(s):  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Induced Subgraphs ◽  
Topological Characterization ◽  
Network Information ◽  
The Subject ◽  
Euclidean Distances ◽  
The Brain

In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas “touches” the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.

Structural magnetic resonance imaging markers of susceptibility and transition to schizophrenia: A review of familial and clinical high risk population studies

2014 ◽  
Vol 29 (2) ◽  
pp. 144-154 ◽  
Author(s):  
C Bois ◽  
HC Whalley ◽  
AM McIntosh ◽  
SM Lawrie
Keyword(s):  
High Risk ◽  
Grey Matter ◽  
Brain Regions ◽  
High Risk Population ◽  
Clinical High Risk ◽  
Cross Sectional ◽  
Structural Abnormalities ◽  
Neuroimaging Data ◽  
Imaging Markers ◽  
The Brain

There is a growing consensus that a symptomatology as complex and heterogeneous as schizophrenia is likely to be produced by widespread perturbations of brain structure, as opposed to isolated deficits in specific brain regions. Structural brain-imaging studies have shown that several features of the brain, such as grey matter, white matter integrity and the morphology of the cortex differ in individuals at high risk of the disorder compared to controls, but to a lesser extent than in patients, suggesting that structural abnormalities may form markers of vulnerability to the disorder. Research has had some success in delineating abnormalities specific to those individuals that transition to psychosis, compared to those at high risk that do not, suggesting that a general risk for the disorder can be distinguished from alterations specific to frank psychosis. In this paper, we review cross-sectional and longitudinal studies of individuals at familial or clinical high risk of the disorder. We conclude that the search for reliable markers of schizophrenia is likely to be enhanced by methods which amalgamate structural neuroimaging data into a coherent framework that takes into account the widespread distribution of brain alterations, and relates this to leading hypotheses of schizophrenia.

scholarly journals Neural Underpinnings of Obesity: The Role of Oxidative Stress and Inflammation in the Brain

Antioxidants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1018
Author(s):  
Caitlyn A. Mullins ◽  
Ritchel B. Gannaban ◽  
Md Shahjalal Khan ◽  
Harsh Shah ◽  
Md Abu B. Siddik ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Energy Homeostasis ◽  
Dorsal Striatum ◽  
Brain Regions ◽  
Therapeutic Interventions ◽  
Low Grade ◽  
Obesity Prevalence ◽  
Beneficial Effects ◽  
The Brain

Obesity prevalence is increasing at an unprecedented rate throughout the world, and is a strong risk factor for metabolic, cardiovascular, and neurological/neurodegenerative disorders. While low-grade systemic inflammation triggered primarily by adipose tissue dysfunction is closely linked to obesity, inflammation is also observed in the brain or the central nervous system (CNS). Considering that the hypothalamus, a classical homeostatic center, and other higher cortical areas (e.g. prefrontal cortex, dorsal striatum, hippocampus, etc.) also actively participate in regulating energy homeostasis by engaging in inhibitory control, reward calculation, and memory retrieval, understanding the role of CNS oxidative stress and inflammation in obesity and their underlying mechanisms would greatly help develop novel therapeutic interventions to correct obesity and related comorbidities. Here we review accumulating evidence for the association between ER stress and mitochondrial dysfunction, the main culprits responsible for oxidative stress and inflammation in various brain regions, and energy imbalance that leads to the development of obesity. Potential beneficial effects of natural antioxidant and anti-inflammatory compounds on CNS health and obesity are also discussed.

scholarly journals The Emerging Role of the Interplay Among Astrocytes, Microglia, and Neurons in the Hippocampus in Health and Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Daniele Lana ◽  
Filippo Ugolini ◽  
Daniele Nosi ◽  
Gary L. Wenk ◽  
Maria Grazia Giovannini
Keyword(s):  
Acute Inflammation ◽  
Brain Aging ◽  
Morphological Changes ◽  
Brain Regions ◽  
Stratum Radiatum ◽  
Low Grade ◽  
Normal Brain ◽  
Chronic Inflammatory Response ◽  
Health And Disease ◽  
The Brain

For over a century, neurons have been considered the basic functional units of the brain while glia only elements of support. Activation of glia has been long regarded detrimental for survival of neurons but more it appears that this is not the case in all circumstances. In this review, we report and discuss the recent literature on the alterations of astrocytes and microglia during inflammaging, the low-grade, slow, chronic inflammatory response that characterizes normal brain aging, and in acute inflammation. Becoming reactive, astrocytes and microglia undergo transcriptional, functional, and morphological changes that transform them into cells with different properties and functions, such as A1 and A2 astrocytes, and M1 and M2 microglia. This classification of microglia and astrocytes in two different, all-or-none states seems too simplistic, and does not correspond to the diverse variety of phenotypes so far found in the brain. Different interactions occur among the many cell populations of the central nervous system in health and disease conditions. Such interactions give rise to networks of morphological and functional reciprocal reliance and dependency. Alterations affecting one cell population reverberate to the others, favoring or dysregulating their activities. In the last part of this review, we present the modifications of the interplay between neurons and glia in rat models of brain aging and acute inflammation, focusing on the differences between CA1 and CA3 areas of the hippocampus, one of the brain regions most susceptible to different insults. With triple labeling fluorescent immunohistochemistry and confocal microscopy (TIC), it is possible to evaluate and compare quantitatively the morphological and functional alterations of the components of the neuron-astrocyte-microglia triad. In the contiguous and interconnected regions of rat hippocampus, CA1 and CA3 Stratum Radiatum, astrocytes and microglia show a different, finely regulated, and region-specific reactivity, demonstrating that glia responses vary in a significant manner from area to area. It will be of great interest to verify whether these differential reactivities of glia explain the diverse vulnerability of the hippocampal areas to aging or to different damaging insults, and particularly the higher sensitivity of CA1 pyramidal neurons to inflammatory stimuli.

scholarly journals Same Brain, Different Look?—The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

2021 ◽  
Vol 10 (21) ◽  
pp. 4987
Author(s):  
Ronja Thieleking ◽  
Rui Zhang ◽  
Maria Paerisch ◽  
Kerstin Wirkner ◽  
Alfred Anwander ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Diffusion Imaging ◽  
Mean Diffusivity ◽  
Brain Regions ◽  
Clinical Diagnostics ◽  
Neuroimaging Data ◽  
Age Range ◽  
The Impact ◽  
The Brain ◽  
Healthy Participants

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

scholarly journals Frequency-specific directed interactions in the human brain network for language

2017 ◽  
Author(s):  
J. M. Schoffelen ◽  
A. Hultén ◽  
N. Lam ◽  
A. Marquand ◽  
J. Uddén ◽  
...  
Keyword(s):  
Language Processing ◽  
Brain Regions ◽  
Brain Network ◽  
Causality Analysis ◽  
Functional Topology ◽  
Using Data ◽  
Beta Frequency ◽  
Bidirectional Interactions ◽  
The Brain

AbstractThe brain’s remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language, while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input, and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that processing different types of linguistic information may depend on the contributions of distinct brain rhythms.One Sentence SummaryCommunication between language relevant areas in the brain is supported by rhythmic synchronization, where different rhythms reflect the direction of information flow.

scholarly journals Adolescent tuning of association cortex in human structural brain networks

2017 ◽  
Author(s):  
František Váša ◽  
Jakob Seidlitz ◽  
Rafael Romero-Garcia ◽  
Kirstie J. Whitaker ◽  
Gideon Rosenthal ◽  
...  
Keyword(s):  
Sharp Decrease ◽  
Brain Regions ◽  
Brain Network ◽  
Association Cortex ◽  
Structural Correlation ◽  
Age Related ◽  
Cortical Regions ◽  
Structural Networks ◽  
The Brain ◽  
Age Related Changes

AbstractMotivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organisation of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N=297 healthy participants, aged 14-24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

scholarly journals Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression

2019 ◽  
Author(s):  
Alican Nalci ◽  
Wenjing Luo ◽  
Thomas T. Liu
Keyword(s):  
Functional Connectivity ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Bold Signal ◽  
Global Signal ◽  
Before And After ◽  
Oxygenation Level ◽  
Motion Measurements ◽  
The Brain

AbstractIn resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.

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