scholarly journals The Role of the Human Brain Neuron–Glia–Synapse Composition in Forming Resting-State Functional Connectivity Networks

2021 ◽  
Vol 11 (12) ◽  
pp. 1565
Author(s):  
Sayan Kahali ◽  
Marcus E Raichle ◽  
Dmitriy A Yablonskiy
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Functional Networks ◽  
Bold Signal ◽  
Resting State Functional Connectivity ◽  
Healthy Human ◽  
Neuronal Density ◽  
Wide Range ◽  
Functional Units ◽  
The Brain

While significant progress has been achieved in studying resting-state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain’s cellular constituents remain. Here, we use quantitative Gradient-Recalled Echo (qGRE) MRI for mapping the human brain cellular composition and BOLD (blood–oxygen level-dependent) MRI to explore how the brain cellular constituents relate to resting-state functional networks. Results show that the BOLD signal-defined synchrony of connections between cellular circuits in network-defined individual functional units is mainly associated with the regional neuronal density, while the between-functional units’ connectivity strength is also influenced by the glia and synaptic components of brain tissue cellular constituents. These mechanisms lead to a rather broad distribution of resting-state functional network properties. Visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of the BOLD signal as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but with a remarkably balanced cellular contents, enabling DMN to have a prominent role in the overall organization of the brain and hierarchy of functional networks.

scholarly journals The Role of the Human Brain Neuron-Glia-Synaptic Composition in Forming Resting State Functional Connectivity Networks

2021 ◽  
Author(s):  
Sayan Kahali ◽  
Marcus E Raichle ◽  
Dmitriy A Yablonskiy
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Functional Networks ◽  
Bold Signal ◽  
Neuronal Density ◽  
Functional Relationships ◽  
Wide Range ◽  
Functional Units ◽  
The Brain

While significant progress has been achieved in studying resting state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain's cellular constituents remain open. In this paper we use quantitative Gradient Recalled Echo (qGRE) MRI for in vivo quantitative mapping of human brain cellular composition, and BOLD (blood oxygen level dependent) MRI resting state data from the Human Connectome Project to explore how the brain cellular constituents relate to resting state functional networks. Our results show that the BOLD-signal-defined synchrony of connections between cellular circuits in network-defined individual functional units is mainly associated with the regional neuronal density, while the strength of the functional connectivity between functional units is influenced not only by the neuronal but also glia and synaptic components of brain tissue cellular constituents. Data show that these cellular-functional relationships are most evident in the infra-slow frequency range (0.01-0.16 Hz) of brain activity, which is known to be linked with fluctuations of the BOLD signal. These mechanisms lead to a rather broad distribution of resting state functional network properties. We found that visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of BOLD signal in individual functional units, as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but a remarkably balanced cellular content enabling DMN prominent role in the overall organization of the brain and the hierarchy of functional networks in health and disease.

scholarly journals The Relationship between Glucose Metabolism, Resting-State fMRI BOLD Signal, and GABAA-Binding Potential: A Preliminary Study in Healthy Subjects and Those with Temporal Lobe Epilepsy

2015 ◽  
Vol 35 (4) ◽  
pp. 583-591 ◽  
Author(s):  
Allison C Nugent ◽  
Ashley Martinez ◽  
Alana D'Alfonso ◽  
Carlos A Zarate ◽  
William H Theodore
Keyword(s):  
Glucose Metabolism ◽  
Temporal Lobe Epilepsy ◽  
Human Brain ◽  
Temporal Lobe ◽  
Resting State ◽  
Healthy Subjects ◽  
Binding Potential ◽  
Bold Signal ◽  
Healthy Human ◽  
The Brain

Glucose metabolism has been associated with magnitude of blood oxygen level-dependent (BOLD) signal and connectivity across subjects within the default mode and dorsal attention networks. Similar correlations within subjects across the entire brain remain unexplored. [18F]-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET), [11C]-flumazenil PET, and resting-state functional magnetic resonance imaging (fMRI) scans were acquired in eight healthy individuals and nine with temporal lobe epilepsy (TLE). Regional metabolic rate of glucose (rMRGlu) was correlated with amplitude of low frequency fluctuations (ALFFs) in the fMRI signal, global fMRI connectivity (GC), regional homogeneity (ReHo), and gamma-aminobutyric acid A—binding potential (GABAA BPND) across the brain. Partial correlations for ALFFs, GC, and ReHo with GABAA BPND were calculated, controlling for rMRGlu. In healthy subjects, significant positive correlations were observed across the brain between rMRGlu and ALFF, ReHo and GABAA BPND, and between ALFFs and GABAA BPND, controlling for rMRGlu. Brain-wide correlations between rMRGlu and ALFFs were significantly lower in TLE patients, and correlations between rMRGlu and GC were significantly greater in TLE than healthy subjects. These results indicate that the glutamatergic and GABAergic systems are coupled across the healthy human brain, and that ALFF is related to glutamate use throughout the healthy human brain. TLE may be a disorder of altered long-range connectivity in association with glutamate function.

scholarly journals Default(y) Mode Network: Important regions of DMN do not survive alterations in flip angles

2020 ◽  
Author(s):  
Ishan Singhal ◽  
Abhishek K. Soni ◽  
Narayanan Srinivasan
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Posterior Cingulate Cortex ◽  
Intrinsic Activity ◽  
Bold Signal ◽  
Resting State Functional Connectivity ◽  
Physiological Noise ◽  
Fmri Study ◽  
The Brain

AbstractThe default mode network (DMN) is thought to capture intrinsic activity of the brain and has been instrumental in understanding the dynamics of the brain. However, the DMN has not been without critics; both conceptual and empirical. The empirical criticisms caution against physiological noise as a source for the reported connectivity in the DMN. Smaller flip angles (FAs) have been modelled and shown to reduce physiological noise in BOLD signal recordings. A previous functional MRI (fMRI) study with flickering checkerboard stimuli, manipulated FAs to show that activity in the posterior-cingulate cortex (PCC) and precuneus is prone to physiological noise. This raises questions about studies that show activations in these areas (PCC and precuneus) with a fixed FA and the role of these areas in brain networks like DMN. Given the prominent role of PCC and precuneus in DMN, we studied the effect of FAs on the resting-state functional connectivity involving these areas in DMN. We used four FAs and recorded resting-state activity in a 3-T scanner. The results show PCC and precuneus BOLD functional connectivity is inconsistent. We lend support to previous empirical criticisms of DMN, linking its activity to physiological noise. Our results add to concerns about PCC and precuneus related BOLD activity and their putative role in DMN. Alongside previous studies we advocate using smaller flip angles as an empirical tool to investigate physiological noise in fMRI studies.

scholarly journals Asymmetry of amygdala resting-state functional connectivity in healthy human brain

Neuroreport ◽  
2020 ◽  
Vol 31 (1) ◽  
pp. 17-21 ◽  
Author(s):  
Alina O. Tetereva ◽  
Vladislav V. Balaev ◽  
Sergey I. Kartashov ◽  
Vadim L. Ushakov ◽  
Alexey M. Ivanitsky ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Healthy Human

scholarly journals Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei-Tang Chang ◽  
Stephanie K. Langella ◽  
Yichuan Tang ◽  
Sahar Ahmad ◽  
Han Zhang ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
A Priori ◽  
Longitudinal Axis ◽  
Functional Networks ◽  
Resting State Functional Connectivity ◽  
Underlying Assumption ◽  
Isotropic Resolution ◽  
Hippocampal Subfields ◽  
Fine Grained

AbstractThe hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.

scholarly journals Is There a Brain Microbiome?

2021 ◽  
Vol 16 ◽  
pp. 263310552110187
Author(s):  
Christopher D Link
Keyword(s):  
Animal Models ◽  
Human Brain ◽  
Neurodegenerative Diseases ◽  
Ground Truth ◽  
Healthy Human ◽  
Strong Motivation ◽  
The Many ◽  
The Brain

Numerous studies have identified microbial sequences or epitopes in pathological and non-pathological human brain samples. It has not been resolved if these observations are artifactual, or truly represent population of the brain by microbes. Given the tempting speculation that resident microbes could play a role in the many neuropsychiatric and neurodegenerative diseases that currently lack clear etiologies, there is a strong motivation to determine the “ground truth” of microbial existence in living brains. Here I argue that the evidence for the presence of microbes in diseased brains is quite strong, but a compelling demonstration of resident microbes in the healthy human brain remains to be done. Dedicated animal models studies may be required to determine if there is indeed a “brain microbiome.”

scholarly journals The Brain Resting-State Functional Connectivity Underlying Violence Proneness: Is It a Reliable Marker for Neurocriminology? A Systematic Review

2019 ◽  
Vol 9 (1) ◽  
pp. 11 ◽  
Author(s):  
Ángel Romero-Martínez ◽  
Macarena González ◽  
Marisol Lila ◽  
Enrique Gracia ◽  
Luis Martí-Bonmatí ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Effective Treatment ◽  
Resting State ◽  
Brain Structures ◽  
Prevention Programs ◽  
Angular Gyrus ◽  
Resting State Functional Connectivity ◽  
Treatment And Prevention ◽  
Reliable Marker ◽  
The Brain

Introduction: There is growing scientific interest in understanding the biological mechanisms affecting and/or underlying violent behaviors in order to develop effective treatment and prevention programs. In recent years, neuroscientific research has tried to demonstrate whether the intrinsic activity within the brain at rest in the absence of any external stimulation (resting-state functional connectivity; RSFC) could be employed as a reliable marker for several cognitive abilities and personality traits that are important in behavior regulation, particularly, proneness to violence. Aims: This review aims to highlight the association between the RSFC among specific brain structures and the predisposition to experiencing anger and/or responding to stressful and distressing situations with anger in several populations. Methods: The scientific literature was reviewed following the PRISMA quality criteria for reviews, using the following digital databases: PubMed, PsycINFO, Psicodoc, and Dialnet. Results: The identification of 181 abstracts and retrieval of 34 full texts led to the inclusion of 17 papers. The results described in our study offer a better understanding of the brain networks that might explain the tendency to experience anger. The majority of the studies highlighted that diminished RSFC between the prefrontal cortex and the amygdala might make people prone to reactive violence, but that it is also necessary to contemplate additional cortical (i.e. insula, gyrus [angular, supramarginal, temporal, fusiform, superior, and middle frontal], anterior and posterior cingulated cortex) and subcortical brain structures (i.e. hippocampus, cerebellum, ventral striatum, and nucleus centralis superior) in order to explain a phenomenon as complex as violence. Moreover, we also described the neural pathways that might underlie proactive violence and feelings of revenge, highlighting the RSFC between the OFC, ventral striatal, angular gyrus, mid-occipital cortex, and cerebellum. Conclusions. The results from this synthesis and critical analysis of RSFC findings in several populations offer guidelines for future research and for developing a more accurate model of proneness to violence, in order to create effective treatment and prevention programs.

scholarly journals Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

2021 ◽  
Vol 12 ◽  
Author(s):  
Yifei Zhang ◽  
Xiaodan Chen ◽  
Xinyuan Liang ◽  
Zhijiang Wang ◽  
Teng Xie ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Human Brain ◽  
Cognitive Performance ◽  
Power Law ◽  
Resting State ◽  
Brain Networks ◽  
Functional Networks ◽  
Whole Brain ◽  
Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

scholarly journals Functional Segregation of Human Brain Networks Across the Lifespan: An Exploratory Analysis of Static and Dynamic Resting-State Functional Connectivity

2020 ◽  
Vol 14 ◽  
Author(s):  
Benjamin M. Rosenberg ◽  
Eva Mennigen ◽  
Martin M. Monti ◽  
Roselinde H. Kaiser
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dwell Time ◽  
Brain Networks ◽  
Older Age ◽  
Functional Network ◽  
Functional Networks ◽  
Resting State Functional Connectivity ◽  
Network State ◽  
High Connectivity

Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity. Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.

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