scholarly journals Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data

2018 ◽  
Vol 115 (9) ◽  
pp. E2105-E2114 ◽  
Author(s):  
Jonathan D. Power ◽  
Mark Plitt ◽  
Stephen J. Gotts ◽  
Prantik Kundu ◽  
Valerie Voon ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Blood Oxygen Level Dependent ◽  
Critical Element ◽  
Blood Oxygen Level ◽  
Brain Organization ◽  
Whole Brain ◽  
Decay Properties ◽  
Signal Decay ◽  
Fmri Time Series

“Functional connectivity” techniques are commonplace tools for studying brain organization. A critical element of these analyses is to distinguish variance due to neurobiological signals from variance due to nonneurobiological signals. Multiecho fMRI techniques are a promising means for making such distinctions based on signal decay properties. Here, we report that multiecho fMRI techniques enable excellent removal of certain kinds of artifactual variance, namely, spatially focal artifacts due to motion. By removing these artifacts, multiecho techniques reveal frequent, large-amplitude blood oxygen level-dependent (BOLD) signal changes present across all gray matter that are also linked to motion. These whole-brain BOLD signals could reflect widespread neural processes or other processes, such as alterations in blood partial pressure of carbon dioxide (pCO2) due to ventilation changes. By acquiring multiecho data while monitoring breathing, we demonstrate that whole-brain BOLD signals in the resting state are often caused by changes in breathing that co-occur with head motion. These widespread respiratory fMRI signals cannot be isolated from neurobiological signals by multiecho techniques because they occur via the same BOLD mechanism. Respiratory signals must therefore be removed by some other technique to isolate neurobiological covariance in fMRI time series. Several methods for removing global artifacts are demonstrated and compared, and were found to yield fMRI time series essentially free of motion-related influences. These results identify two kinds of motion-associated fMRI variance, with different physical mechanisms and spatial profiles, each of which strongly and differentially influences functional connectivity patterns. Distance-dependent patterns in covariance are nearly entirely attributable to non-BOLD artifacts.

scholarly journals Undirected graphs of frequency-dependent functional connectivity in whole brain networks

2005 ◽  
Vol 360 (1457) ◽  
pp. 937-946 ◽  
Author(s):  
Raymond Salvador ◽  
John Suckling ◽  
Christian Schwarzbauer ◽  
Ed Bullmore
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Brain Networks ◽  
Partial Coherence ◽  
Association Cortex ◽  
Undirected Graphs ◽  
Long Distance ◽  
Whole Brain ◽  
Low Frequencies ◽  
Fmri Time Series

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information ϕ , an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.

scholarly journals A Novel Synchronization-Based Approach for Functional Connectivity Analysis

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Angela Lombardi ◽  
Sabina Tangaro ◽  
Roberto Bellotti ◽  
Alessandro Bertolino ◽  
Giuseppe Blasi ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Functional Coupling ◽  
Healthy Population ◽  
Brain Organization ◽  
Functional Brain ◽  
Novel Approach ◽  
Functional Brain Networks ◽  
Fmri Time Series ◽  
Functional Brain Organization

Complex network analysis has become a gold standard to investigate functional connectivity in the human brain. Popular approaches for quantifying functional coupling between fMRI time series are linear zero-lag correlation methods; however, they might reveal only partial aspects of the functional links between brain areas. In this work, we propose a novel approach for assessing functional coupling between fMRI time series and constructing functional brain networks. A phase space framework is used to map couples of signals exploiting their cross recurrence plots (CRPs) to compare the trajectories of the interacting systems. A synchronization metric is extracted from the CRP to assess the coupling behavior of the time series. Since the functional communities of a healthy population are expected to be highly consistent for the same task, we defined functional networks of task-related fMRI data of a cohort of healthy subjects and applied a modularity algorithm in order to determine the community structures of the networks. The within-group similarity of communities is evaluated to verify whether such new metric is robust enough against noise. The synchronization metric is also compared with Pearson’s correlation coefficient and the detected communities seem to better reflect the functional brain organization during the specific task.

scholarly journals Neuronal cascades shape whole-brain functional dynamics at rest

2020 ◽  
Author(s):  
Giovanni Rabuffo ◽  
Jan Fousek ◽  
Christophe Bernard ◽  
Viktor Jirsa
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Blood Oxygen Level Dependent ◽  
Brain Network ◽  
Neural Activation ◽  
Data Set ◽  
Whole Brain ◽  
Dynamic Component ◽  
Leading Role ◽  
Co Activation

AbstractAt rest, mammalian brains display a rich complex spatiotemporal behavior, which is reminiscent of healthy brain function and has provided nuanced understandings of several major neurological conditions. Despite the increasingly detailed phenomenological documentation of the brain’s resting state, its principle underlying causes remain unknown. To establish causality, we link structurally defined features of a brain network model to neural activation patterns and their variability. For the mouse, we use a detailed connectome-based model and simulate the resting state dynamics for neural sources and whole brain imaging signals (Blood-Oxygen-Level-Dependent (BOLD), Electroencephalography (EEG)). Under conditions of near-criticality, characteristic neuronal cascades form spontaneously and propagate through the network. The largest neuronal cascades produce short-lived but robust co-fluctuations at pairs of regions across the brain. During these co-activation episodes, long-lasting functional networks emerge giving rise to epochs of stable resting state networks correlated in time. Sets of neural cascades are typical for a resting state network, but different across. We experimentally confirm the existence and stability of functional connectivity epochs comprising BOLD co-activation bursts in mice (N=19). We further demonstrate the leading role of the neuronal cascades in a simultaneous EEG/fMRI data set in humans (N=15), explaining a large part of the variability of functional connectivity dynamics. We conclude that short-lived neuronal cascades are a major robust dynamic component contributing to the organization of the slowly evolving spontaneous fluctuations in brain dynamics at rest.

scholarly journals Advanced Meditation Alters Resting-State Brain Network Connectivity Correlating With Improved Mindfulness

2021 ◽  
Vol 12 ◽  
Author(s):  
Ramana V. Vishnubhotla ◽  
Rupa Radhakrishnan ◽  
Kestas Kveraga ◽  
Rachael Deardorff ◽  
Chithra Ram ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Connectivity ◽  
Blood Oxygen Level Dependent ◽  
Brain Network ◽  
Resting State Functional Connectivity ◽  
Blood Oxygen Level ◽  
Default Mode ◽  
Before And After ◽  
Brain Network Connectivity

Purpose: The purpose of this study was to investigate the effect of an intensive 8-day Samyama meditation program on the brain functional connectivity using resting-state functional MRI (rs-fMRI).Methods: Thirteen Samyama program participants (meditators) and 4 controls underwent fMRI brain scans before and after the 8-day residential meditation program. Subjects underwent fMRI with a blood oxygen level dependent (BOLD) contrast at rest and during focused breathing. Changes in network connectivity before and after Samyama program were evaluated. In addition, validated psychological metrics were correlated with changes in functional connectivity.Results: Meditators showed significantly increased network connectivity between the salience network (SN) and default mode network (DMN) after the Samyama program (p < 0.01). Increased connectivity within the SN correlated with an improvement in self-reported mindfulness scores (p < 0.01).Conclusion: Samyama, an intensive silent meditation program, favorably increased the resting-state functional connectivity between the salience and default mode networks. During focused breath watching, meditators had lower intra-network connectivity in specific networks. Furthermore, increased intra-network connectivity correlated with improved self-reported mindfulness after Samyama.Clinical Trials Registration: [https://clinicaltrials.gov], Identifier: [NCT04366544]. Registered on 4/17/2020.

scholarly journals Differential effects of anesthetics on resting state functional connectivity in the mouse

2019 ◽  
Vol 40 (4) ◽  
pp. 875-884 ◽  
Author(s):  
Hongyu Xie ◽  
David Y Chung ◽  
Sreekanth Kura ◽  
Kazutaka Sugimoto ◽  
Sanem A Aykan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Chloral Hydrate ◽  
Low Frequency ◽  
Blood Oxygen Level Dependent ◽  
Resting State Functional Connectivity ◽  
Blood Oxygen Level ◽  
Intrinsic Signal ◽  
Optical Intrinsic Signal ◽  
Important Approach

Blood oxygen level-dependent (BOLD) functional MRI (fMRI) is a standard approach to examine resting state functional connectivity (RSFC), but fMRI in animal models is challenging. Recently, functional optical intrinsic signal imaging—which relies on the same hemodynamic signal underlying BOLD fMRI—has been developed as a complementary approach to assess RSFC in mice. Since it is difficult to ensure that an animal is in a truly resting state while awake, RSFC measurements under anesthesia remain an important approach. Therefore, we systematically examined measures of RSFC using non-invasive, widefield optical intrinsic signal imaging under five different anesthetics in male C57BL/6J mice. We find excellent seed-based, global, and interhemispheric connectivity using tribromoethanol (Avertin) and ketamine–xylazine, comparable to results in the literature including awake animals. Urethane anesthesia yielded intermediate results, while chloral hydrate and isoflurane were both associated with poor RSFC. Furthermore, we found a correspondence between the strength of RSFC and the power of low-frequency hemodynamic fluctuations. In conclusion, Avertin and ketamine–xylazine provide robust and reproducible measures of RSFC in mice, whereas chloral hydrate and isoflurane do not.

Functional connectivity during smooth pursuit eye movements

2020 ◽  
Vol 124 (6) ◽  
pp. 1839-1856
Author(s):  
Rebekka Schröder ◽  
Anna-Maria Kasparbauer ◽  
Inga Meyhöfer ◽  
Maria Steffens ◽  
Peter Trautner ◽  
...  
Keyword(s):  
Eye Movements ◽  
Functional Connectivity ◽  
Smooth Pursuit ◽  
Potential Role ◽  
Blood Oxygen Level Dependent ◽  
Smooth Pursuit Eye Movements ◽  
Blood Oxygen Level ◽  
Comprehensive Investigation ◽  
Pursuit Eye Movements ◽  
Healthy Participants

This study provides a comprehensive investigation of blood oxygen level-dependent (BOLD) functional connectivity during smooth pursuit eye movements. Results from a large sample of healthy participants suggest that key oculomotor regions interact closely with each other but also with regions not primarily associated with eye movements. Understanding functional connectivity during smooth pursuit is important, given its potential role as an endophenotype of psychoses.

scholarly journals Subcortical connectivity in dementia with Lewy bodies and Alzheimer's disease

2013 ◽  
Vol 203 (3) ◽  
pp. 209-214 ◽  
Author(s):  
Eva R. Kenny ◽  
John T. O'Brien ◽  
Michael J. Firbank ◽  
Andrew M. Blamire
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Dementia With Lewy Bodies ◽  
Lewy Bodies ◽  
Low Frequency ◽  
Blood Oxygen Level Dependent ◽  
Bold Signal ◽  
Blood Oxygen Level

BackgroundResting-state functional magnetic resonance imaging (fMRI) can be used to measure correlations in spontaneous low-frequency fluctuations in the blood oxygen level-dependent (BOLD) signal which represent functional connectivity between key brain areas.AimsTo investigate functional connectivity with regions hypothesised to be differentially affected in dementia with Lewy bodies (DLB) compared with Alzheimer's disease and controls.MethodFifteen participants with probable DLB, 16 with probable Alzheimer's disease and 16 controls were scanned in the resting-state using a 3T scanner. The BOLD signal time-series of fluctuations in seed regions were correlated with all other voxels to measure functional connectivity.ResultsParticipants with DLB and Alzheimer's disease showed greater caudate and thalamic connectivity compared with controls. Those with DLB showed greater putamen connectivity compared with those with Alzheimer's disease and the controls. No regions showed less connectivity in DLB or Alzheimer's disease v. controls, or in DLB v. Alzheimer's disease.ConclusionsAltered connectivity in DLB and Alzheimer's disease provides new insights into the neurobiology of these disorders and may aid in earlier diagnosis.

scholarly journals Individual Topological Analysis of Synchronization-Based Brain Connectivity

2020 ◽  
Vol 10 (9) ◽  
pp. 3275
Author(s):  
Angela Lombardi ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Alfonso Monaco ◽  
Sabina Tangaro ◽  
...  
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Topological Analysis ◽  
The Other ◽  
Modular Organization ◽  
Connectivity Matrix ◽  
Functional Coupling ◽  
Macro Scale ◽  
Fmri Time Series

Functional connectivity analysis aims at assessing the strength of functional coupling between the signal responses in distinct brain areas. Usually, functional magnetic resonance imaging (fMRI) time series connections are estimated through zero-lag correlation metrics that quantify the statistical similarity between pairs of regions or spectral measures that assess synchronization at a frequency band of interest. Here, we explored the application of a new metric to assess the functional synchronization in phase space between fMRI time series in a resting state. We applied a complete topological analysis to the resulting connectivity matrix to uncover both the macro-scale organization of the brain and detect the most important nodes. The synchronization metric is also compared with Pearson’s correlation coefficient and spectral coherence to highlight similarities and differences between the topologies of the three functional networks. We found that the individual topological organization of the resulting synchronization-based connectivity networks shows a finer modular organization than that identified with the other two metrics and a low overlap with the modular partitions of the other two networks suggesting that the derived topological information is not redundant and could be potentially integrated to provide a multi-scale description of functional connectivity.

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