scholarly journals Contribution of structural connectivity to MEG functional connectivity

2019 ◽  
Author(s):  
Anirudh Wodeyar ◽  
Ramesh Srinivasan
Keyword(s):  
Functional Connectivity ◽  
Graphical Models ◽  
Phase Synchronization ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Gamma Band ◽  
Amplitude Envelope ◽  
Common Input ◽  
Structural Networks ◽  
Envelope Correlation

AbstractStructural connectivity by axonal fiber bundles provides the substrate for transmission of action potentials across the brain. Functional connectivity in MEG signals is expected to arise from communication along structural connections. However, very little empirical evidence has been obtained to support this hypothesis. The main objective of this study is to use simulations and MEG data to directly evaluate the contribution of structural connectivity to MEG functional connectivity measures. Since axonal transmission is on a millisecond time scale we hypothesize that measures sensitive to phase synchronization in a frequency band, such as coherence, would have a closer relationship to structural connectivity than measures sensitive to slower time scales such as amplitude-envelope correlation. We estimate graphical models of MEG functional connectivity, i.e, the MEG effective connectivity, to reduce the influence of leakage effects and common input effects, and to explicitly model the contribution of structural connectivity to functional connectivity. Consistent with our hypothesis, networks defined by models of gamma band (> 30 Hz) coherence that incorporate phase information show the closest alignment to structural connectivity. However, at lower frequencies (1-30 Hz) there was better alignment between models of amplitude envelope correlation and structural connectivity. In simulations, summarizing network properties of graphical models using graph theoretic metrics provides a robust measure of the relationship between functional and structural connectivity that is preserved even at low signal to noise ratios. In MEG data, centrality of nodes in the gamma band networks more closely correspond to centrality of nodes in the structural networks than a direct comparison between edge weights.

scholarly journals Intrinsic functional connectivity resembles cortical architecture at various levels of isoflurane anesthesia

2016 ◽  
Author(s):  
Felix Fischer ◽  
Florian Pieper ◽  
Edgar Galindo-Leon ◽  
Gerhard Engler ◽  
Claus C. Hilgetag ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Occipital Cortex ◽  
Amplitude Envelope ◽  
Cortical Areas ◽  
Temporal Properties ◽  
Connectivity Patterns ◽  
Different Depths ◽  
Amplitude Correlation

AbstractCortical activity patterns change in different depths of general anesthesia. Here we investigate the associated network level changes of functional connectivity. We recorded ongoing electrocorticographic (ECoG) activity from the ferret temporo-parieto-occipital cortex under various levels of isoflurane and determined the functional connectivity by computing amplitude envelope correlations. Through hierarchical clustering, we derived typical connectivity patterns corresponding to light, intermediate and deep anesthesia. Generally, amplitude correlation strength increased strongly with depth of anesthesia across all cortical areas and frequency bands. This was accompanied by the emergence of burstsuppression activity in the ECoG signal and a change of the spectrum of the amplitude envelope. Normalizing the functional connectivity patterns showed that the topographical structure remained similar across depths of anesthesia, resembling the functional association of the underlying cortical areas. Thus, while strength and temporal properties of amplitude co-modulation vary depending on the activity of local neural circuits, their network-level interaction pattern is presumably most strongly determined by the underlying structural connectivity.

scholarly journals Dynamic reconfiguration of macaque brain networks during free-viewing of natural scenes

2021 ◽  
Author(s):  
Michael Ortiz-Rios ◽  
Fabien Belezeau ◽  
Marcus Haag ◽  
Michael C. Schmid ◽  
Marcus Kaiser
Keyword(s):  
Functional Connectivity ◽  
Functional Organization ◽  
Structural Connectivity ◽  
Centrality Measures ◽  
Contrast Imaging ◽  
Network Analyses ◽  
Free Viewing ◽  
Wide Range ◽  
Visual Scenes ◽  
Structural Networks

Natural vision involves the activation of a wide range of higher-level regions processing objects, motion, faces and actions. Here, we pursue a data-driven approach to explore how higher-level visual processes relate to the underlying structural and functional connectivity. Using a free-viewing paradigm in four awake rhesus macaque monkeys, we investigate how different visual scenes changes functional connectivity. Additionally, we explore how such functional connectivity, as measured through fMRI, is related to the structural connectivity, as measured through diffusion weighted imaging. At first, we evaluate the consistency of the elicited free-viewing pattern using standard analytical techniques. We also evaluate the underlying structural connectivity via diffusion data by tracking white matter bundle projections from visual cortex. We then reconstruct free-viewing and structural networks and quantify their properties. Centrality measures over the entire fMRI time-series revealed a consistent functional network engaged during free-viewing that included widespread hub regions across frontal (FEF, 46v), parietal (LIP, Tpt), and occipitotemporal cortex (MT, V4 and TE) among others. Interestingly, a small number of highly-weighted and long-length inter-hemispheric connections indicated the presence of long-range integrative properties during free-viewing. We hypothesized that during free-viewing, networks had the capacity to change their local and distal connections depending on the on-going changes in visual scenes. To capture these network dynamics, we departed from the static modular architecture of the structural networks and demonstrate that hubs in free-viewing networks reorganized according to the presence of objects, motion, and faces in the movie scenes indicating poly-functional properties. Lastly, we compare each NHP subject network and observed high consistency between individuals across same network type with closer correspondence between structural networks (e.g., diffusion based and tract-tracing networks). In summary, our network analyses revealed ongoing changes in large-scale functional organization present during free-viewing in the macaque monkey and highlight the advantages of multi-contrast imaging in awake monkeys for investigating dynamical processes in visual cognition. To further promote the use naturalistic free-viewing paradigms and increase the development of macaque neuroimaging resources, we share our datasets in the PRIME-DE consortium.

scholarly journals Altered structural connectivity networks in dementia with lewy bodies

2021 ◽  
Author(s):  
Nicolas Nicastro ◽  
Elijah Mak ◽  
Ajenthan Surendranathan ◽  
Timothy Rittman ◽  
James B. Rowe ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Dementia With Lewy Bodies ◽  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Lewy Bodies ◽  
Occipital Lobe ◽  
Mri Imaging ◽  
Structural Networks ◽  
Graph Parameters

AbstractThe impairment of large-scale brain networks has been observed in dementia with Lewy bodies (DLB) using functional connectivity, but the potential for an analogous effect on structural covariance patterns has not been determined. Twenty-four probable DLB subjects (mean age 74.3 ± 6.7 years, 16.7% female) and 23 similarly aged Controls were included. All participants underwent 3T MRI imaging with high-resolution T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence. Graph theoretical analyses were performed using variation in regional cortical thickness to construct a structural association matrix with pairwise Pearson correlations. Global and nodal graph parameters were computed to assess between-group differences and community structure was studied in order to quantify large-scale brain networks in both groups. In comparison to Controls, DLB subjects had decreased global efficiency, clustering, modularity and small-worldness of structural networks (all p < 0.05). Nodal measures showed that DLB subjects also had decreased clustering in bilateral temporal regions and decreased closeness centrality in extensive areas including right middle frontal, left cingulate and bilateral occipital lobe (all false-discovery rate (FDR)-corrected q < 0.05). Whereas four distinct modules could be clearly identified in Controls, DLB showed extensively disorganized modules, including default-mode network and dorsal attentional network. Our results suggest a marked impairment in large-scale brain structural networks in DLB, mirroring functional connectivity networks disruption.

Tuning the brain for novelty detection under emotional threat: the role of specific gamma-band phase synchronization

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S42
Author(s):  
M Garcia-Garcia ◽  
J Yordanova ◽  
V Kolev ◽  
J Dominguez-Borras ◽  
C Escera
Keyword(s):  
Phase Synchronization ◽  
Novelty Detection ◽  
Gamma Band ◽  
The Brain

Selection for Action: The Medial Frontal Cortex Is an Executive Hub for Stimulus and Response Selection

2021 ◽  
pp. 1-28
Author(s):  
Dariusz Asanowicz ◽  
Bartłomiej Panek ◽  
Ilona Kotlewska
Keyword(s):  
Functional Connectivity ◽  
Frontal Cortex ◽  
Phase Synchronization ◽  
Visual Stimulation ◽  
Response Selection ◽  
Flanker Task ◽  
Medial Frontal Cortex ◽  
Conflict Condition ◽  
Underlying Processes ◽  
Selection For Action

Abstract This EEG study investigates the electrophysiological activity underlying processes of stimulus and response selection, and their executive orchestration via long-range functional connectivity under conflict condition, in order to shed more light on how these brain dynamics shape individual behavioral performance. Participants (n = 91) performed a modified flanker task, in which bilateral visual stimulation and a bimanual response pattern were employed to isolate the stimulus and response selection-related lateralized activity. First, we identified conflict-related markers of task-relevant processes; most importantly, the stimulus and response selection were evidenced by contra–ipsilateral differences in visual and motor activity, respectively, and executive control was evidenced by modulations of midfrontal activity. Second, we identified conflict-related functional connectivity between midfrontal and other task-relevant areas. The results showed that interregional phase synchronization in theta band was centered at the midfrontal site, interpreted here as a “hub” of executive communication. Importantly, the theta functional connectivity was more robust under the condition of increased demands for stimulus and response selection, including connectivity between the medial frontal cortex and the lateral frontal and motor areas, as well as cross-frequency theta–alpha coupling between the medial frontal cortex and contralateral visual areas. Third, we showed that individual differences in the measured conflict-related EEG activity, particularly the midfrontal N2, theta power, and global theta connectivity, predict the behavioral efficiency in conflict resolution.

scholarly journals Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Reaction Times ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Human Connectome Project ◽  
Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

scholarly journals qEEG Analysis in the Diagnosis of Alzheimers Disease; a Comparison of Functional Connectivity and Spectral Analysis

2022 ◽  
Author(s):  
Maria Semeli Frangopoulou ◽  
Maryam Alimardani
Keyword(s):  
Spectral Analysis ◽  
Functional Connectivity ◽  
Data Science ◽  
Phase Synchronization ◽  
Connectivity Analysis ◽  
Alzheimers Disease ◽  
Brain Disorder ◽  
Alternative Analysis ◽  
Functional Connectivity Analysis ◽  
Analysis Methods

Alzheimers disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain, causing a decline in cognitive abilities and difficulties in engaging in day-to-day activities. This study compares an FFT-based spectral analysis against a functional connectivity analysis based on phase synchronization, for finding known differences between AD patients and Healthy Control (HC) subjects. Both of these quantitative analysis methods were applied on a dataset comprising bipolar EEG montages values from 20 diagnosed AD patients and 20 age-matched HC subjects. Additionally, an attempt was made to localize the identified AD-induced brain activity effects in AD patients. The obtained results showed the advantage of the functional connectivity analysis method compared to a simple spectral analysis. Specifically, while spectral analysis could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting that the AD patients brains are in a phase-locked state. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized in the centro-parietal and centro-temporal areas in the theta frequency band (4-8 Hz). The contribution of this study is that it applies a neural metric for Alzheimers detection from a data science perspective rather than from a neuroscience one. The study shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

2019 ◽  
Author(s):  
Milou Straathof ◽  
Michel R.T. Sinke ◽  
Theresia J.M. Roelofs ◽  
Erwin L.A. Blezer ◽  
R. Angela Sarabdjitsingh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Rat Brain ◽  
Resting State ◽  
Structural Connectivity ◽  
Distinct Structure ◽  
Macro Scale ◽  
Function Relationship ◽  
The Brain ◽  
Meso Scale

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.

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