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JournalISSN: 2158-0014

Brain connectivity 

Mary Ann Liebert, Inc.
About: Brain connectivity is an academic journal published by Mary Ann Liebert, Inc.. The journal publishes majorly in the area(s): Resting state fMRI & Functional magnetic resonance imaging. It has an ISSN identifier of 2158-0014. Over the lifetime, 753 publications have been published receiving 28918 citations.


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Journal ArticleDOI
TL;DR: The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fc MRI measures.
Abstract: Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...

3,388 citations

Journal ArticleDOI
TL;DR: The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses.
Abstract: Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

2,822 citations

Journal ArticleDOI
TL;DR: A problem that is still treated lightly despite its significant impact on RS-FMRI inferences is discussed; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness.
Abstract: Resting-state functional magnetic resonance imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but is not easy to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated that GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem that GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter interregional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true interregional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network's regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.

834 citations

Journal ArticleDOI
TL;DR: Results of Eklund and associates' study show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results.
Abstract: Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that existing parametric methods for determining statistically significant clusters had greatly inflated FPRs (“up to 70%,” mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussian shaped and stationary), calling into question potentially “countless” previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed “particularly high” FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis...

600 citations

Journal ArticleDOI
TL;DR: In this essay, some of the unique features of intrinsic activity are reviewed, as it relates to the understanding of brain organization, which exhibits a surprising level of organization that emerges with dimensions of both space and time.
Abstract: The pressing need to better understand human brain organization is appreciated by all who have labored to explain the uniqueness of human behavior in health and disease. Early work on the cytoarchitectonics of the human brain by Brodmann and others accompanied by several centuries of lesion behavior work, although valuable, has left us far short of what we need. Fortunately, modern brain imaging techniques have, over the past 40 years, substantially changed the situation by permitting the safe appraisal of both anatomical and functional relationships within the living human brain. An unexpected feature of this work is the critical importance of ongoing, intrinsic activity, which accounts for the majority of brain's energy consumption and exhibits a surprising level of organization that emerges with dimensions of both space and time. In this essay, some of the unique features of intrinsic activity are reviewed, as it relates to our understanding of brain organization.

597 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202326
202282
2021119
202049
201966
201850