scholarly journals Brain activity fluctuations propagate as waves traversing the cortical hierarchy

2020 ◽  
Author(s):  
Yameng Gu ◽  
Lucas E. Sainburg ◽  
Sizhe Kuang ◽  
Feng Han ◽  
Jack W. Williams ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Hierarchical Organization ◽  
Subcortical Structures ◽  
Cortical Hierarchy ◽  
Neural Origin ◽  
The Brain

AbstractThe brain exhibits highly organized patterns of spontaneous activity as measured by resting-state fMRI fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, though this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

scholarly journals Brain Activity Fluctuations Propagate as Waves Traversing the Cortical Hierarchy

2021 ◽  
Author(s):  
Yameng Gu ◽  
Lucas E Sainburg ◽  
Sizhe Kuang ◽  
Feng Han ◽  
Jack W Williams ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Hierarchical Organization ◽  
Subcortical Structures ◽  
Cortical Hierarchy ◽  
Neural Origin ◽  
The Brain

Abstract The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state functional magnetic resonance imaging (fMRI) fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, although this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here, we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

scholarly journals Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

2020 ◽  
pp. 1-21
Author(s):  
Alexandra Anagnostopoulou ◽  
Charis Styliadis ◽  
Panagiotis Kartsidis ◽  
Evangelia Romanopoulou ◽  
Vasiliki Zilidou ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Functional Connectivity ◽  
Cognitive Training ◽  
Resting State ◽  
Network Architecture ◽  
Right Hemisphere ◽  
Random Network ◽  
Brain Activity ◽  
Hierarchical Organization ◽  
The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

scholarly journals Lag threads organize the brain’s intrinsic activity

2015 ◽  
Vol 112 (17) ◽  
pp. E2235-E2244 ◽  
Author(s):  
Anish Mitra ◽  
Abraham Z. Snyder ◽  
Tyler Blazey ◽  
Marcus E. Raichle
Keyword(s):  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Slow Activity ◽  
Intrinsic Brain Activity ◽  
Temporal Sequences ◽  
The Brain

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.

scholarly journals Spatiotemporal Trajectories in Resting-state FMRI Revealed by Convolutional Variational Autoencoder

2021 ◽  
Author(s):  
Xiaodi Zhang ◽  
Eric Maltbie ◽  
Shella Keilholz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Spatiotemporal Patterns ◽  
Network Activity ◽  
Fmri Data ◽  
Variational Autoencoder ◽  
Spatiotemporal Trajectories ◽  
The Brain

AbstractRecent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.

scholarly journals Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation

2021 ◽  
Author(s):  
Toshiki Kusano ◽  
Hiroki Kurashige ◽  
Isao Nambu ◽  
Yoshiya Moriguchi ◽  
Takashi Hanakawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Body Part ◽  
Resting State Fmri ◽  
Body Parts ◽  
Finger Movements ◽  
Multiple Components ◽  
Brain Activity Pattern ◽  
Motor Representations ◽  
The Brain

AbstractSeveral functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

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.

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

2020 ◽  
pp. 169-186
Author(s):  
S. Vidhusha ◽  
A. Kavitha
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Cognitive Task ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Control Group ◽  
Neural Connectivity ◽  
Activation Patterns ◽  
The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

scholarly journals Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chen Chen ◽  
Jian Zhang ◽  
Xiao-Wei Li ◽  
Wenqing Xia ◽  
Xu Feng ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Chronic Tinnitus ◽  
Healthy Controls ◽  
Tinnitus Distress ◽  
The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

scholarly journals Hierarchical disruption in the cortex of anesthetized monkeys as a new signature of consciousness loss

2020 ◽  
Author(s):  
Camilo Miguel Signorelli ◽  
Lynn Uhrig ◽  
Morten Kringelbach ◽  
Bechir Jarraya ◽  
Gustavo Deco
Keyword(s):  
Neural Activity ◽  
Resting State ◽  
Resting State Fmri ◽  
Hierarchical Organization ◽  
Brain Dynamics ◽  
Depth Of Anesthesia ◽  
Functional Changes ◽  
Cortical Hierarchy ◽  
Brain States ◽  
Structure Similarity

AbstractAnesthesia induces a reconfiguration of the repertoire of functional brain states leading to a high function-structure similarity. However, it is unclear how these functional changes lead to loss of consciousness. Here we suggest that the mechanism of conscious access is related to a general dynamical rearrangement of the intrinsic hierarchical organization of the cortex. To measure cortical hierarchy, we applied the Intrinsic Ignition analysis to resting-state fMRI data acquired in awake and anesthetized macaques. Our results reveal the existence of spatial and temporal hierarchical differences of neural activity within the macaque cortex, with a strong modulation by the depth of anesthesia and the employed anesthetic agent. Higher values of Intrinsic Ignition correspond to rich and flexible brain dynamics whereas lower values correspond to poor and rigid, structurally driven brain dynamics. Moreover, spatial and temporal hierarchical dimensions are disrupted in a different manner, involving different hierarchical brain networks. All together suggest that disruption of brain hierarchy is a new signature of consciousness loss.

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