scholarly journals Structure-informed functional connectivity driven by identifiable and state-specific control regions

2021 ◽  
pp. 1-37
Author(s):  
Benjamin Chiêm ◽  
Frédéric Crevecoeur ◽  
Jean-Charles Delvenne
Keyword(s):  
Functional Connectivity ◽  
Brain Regions ◽  
Ongoing Task ◽  
Linear Diffusion ◽  
Potential Control ◽  
Diffusion Dynamics ◽  
Functional Patterns ◽  
The Brain ◽  
Specific Control ◽  
Control Regions

Abstract Describing how the brain anatomical wiring contributes to the emergence of coordinated neural activity underlying complex behavior remains challenging. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as Functional Connectivity (FC), can be explained by a controllable linear diffusion dynamics defined on the brain architecture. Our model, termed structure-informed FC, is based on the hypothesis that different sets of brain regions controlling the information flow on the anatomical wiring produce state-specific functional patterns. We thus introduce a principled framework for the identification of potential control centers in the brain. We find that well-defined, sparse and robust sets of control regions, partially overlapping across several tasks and resting-state, produce FC patterns comparable to empirical ones. Our findings suggest that controllability is a fundamental feature allowing the brain to reach different states.

scholarly journals Structure-informed functional connectivity driven by identifiable and state-specific control regions

2020 ◽  
Author(s):  
Benjamin Chiêm ◽  
Frédéric Crevecoeur ◽  
Jean-Charles Delvenne
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Anatomy ◽  
Linear Diffusion ◽  
Potential Control ◽  
Diffusion Dynamics ◽  
The Brain ◽  
Specific Control ◽  
Control Regions

AbstractA challenge in neuroscience is to describe the contribution of the brain anatomical wiring to the emergence of coordinated neural activity underlying complex behavior. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as Functional Connectivity (FC), can be explained by a linear diffusion dynamics defined on the brain architecture and driven by control regions. Our model, termed structure-informed FC, is based on a novel interpretation of functional connectivity according to which different sets of brain regions controlling the information flow on a fixed anatomical wiring enable the emergence of state-specific FC. This observation leads us to introduce a framework for the identification of potential control centers in the brain. We find that well-defined, sparse and robust sets of control regions, which partially overlap across several task conditions and resting-state, produce FC patterns comparable to empirical ones. In conclusion, this work introduces a principled method for identifying brain regions underlying the task-specific control of brain activity.Significance statementUnderstanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.

scholarly journals Functional Connectivity within Brain Networks of Long Term and Short term Meditators

2019 ◽  
Vol 9 (2S) ◽  
pp. 29-34
Keyword(s):  
Functional Connectivity ◽  
Temporal Correlation ◽  
Brain Regions ◽  
Invasive Technique ◽  
Long Term Effects ◽  
Short Term ◽  
Statistical Concept ◽  
The Brain ◽  
Meditative Practices

Meditation refers to a state of mind of relaxation and concentration, where generally the mind and body is at rest. The process of meditation reflects the state of the brain which is distinct from sleep or typical wakeful states of consciousness. Meditative practices usually involve regulation of emotions and monitoring of attention. Over the past decade there has been a tremendous increase in an interest to study the neural mechanisms involved in meditative practices. It could also be beneficial to explore if the effect of meditation is altered by the number of years of meditation practice. Functional Magnetic Resonance Imaging (fMRI) is a very useful imaging technique which can be used to perform this analysis due to its inherent benefits, mainly it being a non-invasive technique. Functional activation and connectivity analysis can be performed on the fMRI data to find the active regions and the connectivity in the brain regions. Functional connectivity is defined as a simple temporal correlation between anatomically separate, active neural regions. Functional connectivity gives the statistical dependencies between regional time series. It is a statistical concept and is quantified using metrics like Correlation. In this study, a comparison is made between functional connectivity in the brain regions of long term meditation practitioners (LTP) and short-term meditation practitioners (STP) to see the differences and similarities in the connectivity patterns. From the analysis, it is evident that in fact there is a difference in connectivity between long term and short term practitioners and hence continuous practice of meditation can have long term effects.

Functional Connectivity Magnetic Resonance Imaging Reveals Rapid and Reversible Changes in the Brain Following Induction of Psoriasiform Dermatitis in Mice

2018 ◽  
Vol 3 (2) ◽  
pp. 59-64
Author(s):  
Xiping Liu ◽  
Yasutomo Imai ◽  
Yan Zhou ◽  
Sebastian Yu ◽  
Rupeng Li ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Skin Inflammation ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Mri Imaging ◽  
Resonance Imaging ◽  
Mouse Ear ◽  
The Brain

Functional connectivity magnetic resonance imaging (fcMRI), a specific form of MRI imaging, quantitatively assesses connectivity between brain regions that share functional properties. Functional connectivity magnetic resonance imaging has already provided unique insights into changes in the brain in patients with conditions such as depression and pain and symptoms that have been reported by patients with psoriasis and are known to impact quality of life. To identify the central neurological impact of psoriasiform inflammation of the skin, we applied fcMRI analysis to mice that had been topically treated with the Toll-like receptor agonist, imiquimod (IMQ) to induce psoriasiform dermatitis. Brain insula regions, due to their suggested role in stress, were chosen as seed regions for fcMRI analysis. Mouse ear and head skin developed psoriasiform epidermal thickening (up to 4-fold, P < .05) and dermal inflammation after 4 days of topical treatment with IMQ. After fcMRI analysis, IMQ-treated mice showed significantly increased insula fc with wide areas throughout the brain, including, but not limited to, the somatosensory cortex, anterior cingulate cortex, and caudate putamen ( P < .005). This reflects a potential central neurological impact of IMQ-induced psoriasis-like skin inflammation. These data indicate that fcMRI may be valuable tool to quantitatively assess the neurological impact of skin inflammation in patients with psoriasis.

scholarly journals Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

2021 ◽  
Vol 15 ◽  
Author(s):  
Na Xu ◽  
Wei Shan ◽  
Jing Qi ◽  
Jianping Wu ◽  
Qun Wang
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Preoperative Evaluation ◽  
Brain Regions ◽  
Electrophysiological Recording ◽  
Epileptogenic Zone ◽  
Resting State Functional Connectivity ◽  
Presurgical Evaluation ◽  
Resting State Connectivity ◽  
The Brain

Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source–space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.

scholarly journals Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhibao Li ◽  
Chong Liu ◽  
Qiao Wang ◽  
Kun Liang ◽  
Chunlei Han ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Functional Connectivity ◽  
Frequency Band ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Frequency Bands ◽  
The Brain ◽  
Deep Brain

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p &lt; 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p &lt; 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p &lt; 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

scholarly journals Brain hubs defined in the group do not overlap with regions of high inter-individual variability

2021 ◽  
Author(s):  
Derek Martin Smith ◽  
Brian T Kraus ◽  
Ally Dworetsky ◽  
Evan M Gordon ◽  
Caterina Gratton
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Critical Role ◽  
Brain Regions ◽  
Individual Variability ◽  
Spatial Proximity ◽  
Functional Magnetic Resonance ◽  
Multiple Networks ◽  
Human Connectome Project ◽  
The Brain

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability, to better understand if hubs are (a) relatively conserved across people, (b) locations with malleable connectivity, leading individuals to show variable hub profiles, or (c) artifacts arising from cross-person variation. To answer this question, we compared the locations of hubs and regions of strong idiosyncratic functional connectivity ("variants") in both the Midnight Scan Club and Human Connectome Project datasets. Group hubs defined based on the participation coefficient did not overlap strongly with variants. These hubs have relatively strong similarity across participants and consistent cross-network profiles. Consistency across participants was further improved when participation coefficient hubs were allowed to shift slightly in local position. Thus, our results demonstrate that group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density, which are based on spatial proximity and show higher correspondence to locations of individual variability.

scholarly journals Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Frigyes Samuel Racz ◽  
Orestis Stylianou ◽  
Peter Mukli ◽  
Andras Eke
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Brain Activity ◽  
Surrogate Data ◽  
Brain Regions ◽  
Local Dynamic ◽  
Dynamic Functional Connectivity ◽  
Multifractal Scaling ◽  
The Brain

Abstract Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

Task-fMRI Group and Functional Connectivity Analysis of the Brain During Faradarmani Consciousness Field Connection

2021 ◽  
Author(s):  
Mohammad Ali Taheri ◽  
Sara Torabi ◽  
Noushin Nabavi ◽  
Fatemeh Modarresi-Asem ◽  
Majid Abbasi Sisara ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Frontal Lobe ◽  
Critical Role ◽  
Group Analysis ◽  
Brain Regions ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis ◽  
Cognitive Activities ◽  
The Brain

Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

scholarly journals A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 75
Author(s):  
Usman Mahmood ◽  
Zening Fu ◽  
Vince D. Calhoun ◽  
Sergey Plis
Keyword(s):  
Deep Learning ◽  
Functional Connectivity ◽  
State Of The Art ◽  
Weighted Graph ◽  
Brain Regions ◽  
Classification Performance ◽  
Class Discrimination ◽  
Post Hoc ◽  
Deep Learning Model ◽  
The Brain

Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of functional magnetic resonance imaging (fMRI) correlation matrix. However, most of the work with the FC depends on the way the connectivity is computed, and it further depends on the manual post-hoc analysis of the FC matrices. In this work, we propose a deep learning architecture BrainGNN that learns the connectivity structure as part of learning to classify subjects. It simultaneously applies a graphical neural network to this learned graph and learns to select a sparse subset of brain regions important to the prediction task. We demonstrate that the model’s state-of-the-art classification performance on a schizophrenia fMRI dataset and demonstrate how introspection leads to disorder relevant findings. The graphs that are learned by the model exhibit strong class discrimination and the sparse subset of relevant regions are consistent with the schizophrenia literature.

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