scholarly journals Characterization of functional diversity of the human brain based on intrinsic connectivity networks

2018 ◽  
Author(s):  
Congying Chu ◽  
Lingzhong Fan ◽  
Tianzi Jiang
Keyword(s):  
Human Brain ◽  
Functional Diversity ◽  
Functional Properties ◽  
Large Scale ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Brain Regions ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
The Brain

AbstractSpontaneous fluctuations underlying the brain activity can reflect the intrinsic organization of the system, such as the functional brain networks. In large scale, a network perspective has emerged as a new avenue to explore the functional properties of human brain. Here, we studied functional diversity in healthy subjects based on the network perspective. We hypothesized that the patterns of participation of different functional networks were related with the functional diversity of particular brain regions. Independent component analysis (ICA) was adopted to detect the intrinsic connectivity networks (ICNs) based on the data of resting-state functional MRI. An index of functional diversity (FD index) was proposed to quantitatively describe the degree of anisotropic distribution related with participation of various ICNs. We found that FD index continuously varied across the brain, for example, the primary motor cortex with low FD value and the precuneus with significantly high FD value. The FD values indicated the different functional roles of the corresponding brain regions, which were reflected by the various patterns of participation of ICNs. The FD index can be used as a new approach to quantitatively characterize the functional diversity of human brain, even for the changed functional properties caused by the psychiatric disorders.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

scholarly journals Identifying components that vary in space and time from resting-state functional MRI

2021 ◽  
Author(s):  
Gregory Scott ◽  
Robert Leech
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Building Blocks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Time Varying ◽  
Spatiotemporal Evolution ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks

A widespread assumption of fMRI-derived large-scale intrinsic connectivity networks (ICNs) is that they are spatially static over time. However, the assumption of spatial stationarity of ICNs has been challenged by a range of techniques that allow for time-varying connectivity between brain regions and demonstration that canonical networks like the default model network (DMN) can be fractionated according to time-varying connectivity relationships of their subcomponents. Previously, we developed a simple spatiotemporal ICA (stICA) technique to allow the discovery of patterns of spatiotemporal evolution in task fMRI data in a way that avoided the traditional constraint of spatial stationarity on brain networks, and we validated the approach in fMRI of task-to-rest transitions. Here, we apply our stICA technique to resting-state fMRI datasets to explore whether spatiotemporally evolving components of brain activity can be identified in the absence of an overt behavioural task. We found that stICA components could generally be described in terms of graded onsets and offsets of ICNs that had been calculated based on techniques that assumed spatial stationarity. Our results suggest that, to a reasonable approximation, stable ICNs can be taken to be building blocks of the spatiotemporal patterns measured with resting-state fMRI.

scholarly journals Mapping Pavlovian Conditioning Effects on the Brain: Blocking, Contiguity, and Excitatory Effects

2001 ◽  
Vol 86 (2) ◽  
pp. 809-823 ◽  
Author(s):  
Dirk Jones ◽  
F. Gonzalez-Lima
Keyword(s):  
Pavlovian Conditioning ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Blocking Effect ◽  
Anterior Cingulate ◽  
Caudate Putamen ◽  
Fdg Uptake ◽  
The Brain ◽  
Previous Learning

Pavlovian conditioning effects on the brain were investigated by mapping rat brain activity with fluorodeoxyglucose (FDG) autoradiography. The goal was to map the effects of the same tone after blocking or eliciting a conditioned emotional response (CER). In the tone-blocked group, previous learning about a light blocked a CER to the tone. In the tone-excitor group, the same pairings of tone with shock US resulted in a CER to the tone in the absence of previous learning about the light. A third group showed no CER after pseudorandom presentations of these stimuli. Brain systems involved in the various associative effects of Pavlovian conditioning were identified, and their functional significance was interpreted in light of previous FDG studies. Three conditioning effects were mapped: 1) blocking effects: FDG uptake was lower in medial prefrontal cortex and higher in spinal trigeminal and cuneate nuclei in the tone-blocked group relative to the tone-excitor group. 2) Contiguity effects: relative to pseudorandom controls, similar FDG uptake increases in the tone-blocked and -excitor groups were found in auditory regions (inferior colliculus and cortex), hippocampus (CA1), cerebellum, caudate putamen, and solitary nucleus. Contiguity effects may be due to tone-shock pairings common to the tone-blocked and -excitor groups rather than their different CER. And 3) excitatory effects: FDG uptake increases limited to the tone-excitor group occurred in a circuit linked to the CER, including insular and anterior cingulate cortex, vertical diagonal band nucleus, anterior hypothalamus, and caudoventral caudate putamen. This study provided the first large-scale map of brain regions underlying the Kamin blocking effect on conditioning. In particular, the results suggest that suppression of prefrontal activity and activation of unconditioned stimulus pathways are important neural substrates of the Kamin blocking effect.

scholarly journals Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ni Shu ◽  
Yaou Liu ◽  
Yunyun Duan ◽  
Kuncheng Li
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Functional Integration ◽  
Brain Regions ◽  
Hemispheric Asymmetries ◽  
Characteristic Path Length ◽  
Topological Measures ◽  
Structural Connections ◽  
The Brain

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.

scholarly journals Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

2017 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov
Keyword(s):  
Computational Model ◽  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Network ◽  
Ion Concentration ◽  
Brain State ◽  
The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

scholarly journals Increasing and decreasing interregional brain coupling increases and decreases oscillatory activity in the human brain

2021 ◽  
Vol 118 (37) ◽  
pp. e2100652118
Author(s):  
Alejandra Sel ◽  
Lennart Verhagen ◽  
Katharina Angerer ◽  
Raluca David ◽  
Miriam C. Klein-Flügge ◽  
...  
Keyword(s):  
Human Brain ◽  
Alpha Rhythm ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Motor Task ◽  
Brain Regions ◽  
Spike Timing ◽  
Oscillatory Activity ◽  
The Impact ◽  
The Brain

The origins of oscillatory activity in the brain are currently debated, but common to many hypotheses is the notion that they reflect interactions between brain areas. Here, we examine this possibility by manipulating the strength of coupling between two human brain regions, ventral premotor cortex (PMv) and primary motor cortex (M1), and examine the impact on oscillatory activity in the motor system measurable in the electroencephalogram. We either increased or decreased the strength of coupling while holding the impact on each component area in the pathway constant. This was achieved by stimulating PMv and M1 with paired pulses of transcranial magnetic stimulation using two different patterns, only one of which increases the influence exerted by PMv over M1. While the stimulation protocols differed in their temporal patterning, they were comprised of identical numbers of pulses to M1 and PMv. We measured the impact on activity in alpha, beta, and theta bands during a motor task in which participants either made a preprepared action (Go) or withheld it (No-Go). Augmenting cortical connectivity between PMv and M1, by evoking synchronous pre- and postsynaptic activity in the PMv–M1 pathway, enhanced oscillatory beta and theta rhythms in Go and No-Go trials, respectively. Little change was observed in the alpha rhythm. By contrast, diminishing the influence of PMv over M1 decreased oscillatory beta and theta rhythms in Go and No-Go trials, respectively. This suggests that corticocortical communication frequencies in the PMv–M1 pathway can be manipulated following Hebbian spike-timing–dependent plasticity.

scholarly journals HIV Infection Is Associated with Attenuated Frontostriatal Intrinsic Connectivity: A Preliminary Study

2015 ◽  
Vol 21 (3) ◽  
pp. 203-213 ◽  
Author(s):  
Jonathan C. Ipser ◽  
Gregory G. Brown ◽  
Amanda Bischoff-Grethe ◽  
Colm G. Connolly ◽  
Ronald J. Ellis ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Brain Regions ◽  
Group Differences ◽  
Hiv Rna ◽  
Functional Connections ◽  
Intrinsic Connectivity ◽  
Dorsolateral Prefrontal ◽  
Independent Replication ◽  
Subcortical Regions ◽  
The Brain

AbstractHIV-associated cognitive impairments are prevalent, and are consistent with injury to both frontal cortical and subcortical regions of the brain. The current study aimed to assess the association of HIV infection with functional connections within the frontostriatal network, circuitry hypothesized to be highly vulnerable to HIV infection. Fifteen HIV-positive and 15 demographically matched control participants underwent 6 min of resting-state functional magnetic resonance imaging (RS-fMRI). Multivariate group comparisons of age-adjusted estimates of connectivity within the frontostriatal network were derived from BOLD data for dorsolateral prefrontal cortex (DLPFC), dorsal caudate and mediodorsal thalamic regions of interest. Whole-brain comparisons of group differences in frontostriatal connectivity were conducted, as were pairwise tests of connectivity associations with measures of global cognitive functioning and clinical and immunological characteristics (nadir and current CD4 count, duration of HIV infection, plasma HIV RNA). HIV – associated reductions in connectivity were observed between the DLPFC and the dorsal caudate, particularly in younger participants (<50 years, N=9). Seropositive participants also demonstrated reductions in dorsal caudate connectivity to frontal and parietal brain regions previously demonstrated to be functionally connected to the DLPFC. Cognitive impairment, but none of the assessed clinical/immunological variables, was also associated with reduced frontostriatal connectivity. In conclusion, our data indicate that HIV is associated with attenuated intrinsic frontostriatal connectivity. Intrinsic connectivity of this network may therefore serve as a marker of the deleterious effects of HIV infection on the brain, possibly via HIV-associated dopaminergic abnormalities. These findings warrant independent replication in larger studies. (JINS, 2015, 21, 1–11)

scholarly journals Estimation of the Directions of Alpha Rhythm Elementary Sources Using the Method of Human Brain Functional Tomography Based On the Magnetic Encephalography Data

2018 ◽  
Vol 13 (2) ◽  
pp. 426-436 ◽  
Author(s):  
M.N. Ustinin ◽  
S.D. Rykunov ◽  
A.I. Boyko ◽  
O.A. Maslova ◽  
K.D. Walton ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Time Series ◽  
Human Brain ◽  
Alpha Rhythm ◽  
Brain Activity ◽  
Problem Solution ◽  
New York University ◽  
Magnetic Encephalography ◽  
The Brain ◽  
Calculation Grid

New method for the magnetic encephalography data analysis was proposed. The method transforms multichannel time series into the spatial structure of the human brain activity. In this paper we further develop this method to determine the dominant direction of the electrical sources of brain activity at each node of the calculation grid. We have considered the experimental data, obtained with three 275-channel magnetic encephalographs in New York University, McGill University and Montreal University. The human alpha rhythm phenomenon was selected as a model object. Magnetic encephalograms of the brain spontaneous activity were registered for 5-7 minutes in magnetically shielded room. Detailed multichannel spectra were obtained by the Fourier transform of the whole time series. For all spectral components, the inverse problem was solved in elementary current dipole model and the functional structure of the brain activity was calculated in the frequency band 8-12 Hz. In order to estimate the local activity direction, at the each node of calculation grid the vector of the inverse problem solution was selected, having the maximal spectral power. So, the 3D-map of the brain activity vector field was produced – the directional functional tomogram. Such maps were generated for 15 subjects and some common patterns were revealed in the directions of the alpha rhythm elementary sources. The proposed method can be used to study the local properties of the brain activity in any spectral band and in any brain compartment.

scholarly journals Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

2018 ◽  
Author(s):  
RL van den Brink ◽  
S Nieuwenhuis ◽  
TH Donner
Keyword(s):  
Spatial Distribution ◽  
Human Brain ◽  
Spatial Patterns ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Heterogeneous Distribution ◽  
Neurotransmitter Systems ◽  
The Brain ◽  
Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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