A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

Frontiers in Systems Neuroscience, Год журнала: 2016, Номер 9

Опубликована: Янв. 8, 2016

Oscillatory neuronal activity may provide a mechanism for dynamic network coordination. Rhythmic interactions can be quantified using multiple metrics, each with their own advantages and disadvantages. This tutorial will review summarize current analysis methods used in the field of invasive non-invasive electrophysiology to study connections between populations. First, we metrics functional connectivity, including coherence, phase synchronization, phase-slope index, Granger causality, specific aim an intuition how these work, as well quantitative definition. Next, highlight number interpretational caveats common pitfalls that arise when performing connectivity analysis, reference problem, signal noise ratio volume conduction input trial sample size bias problem. These illustrated by presenting set MATLAB-scripts, which executed reader simulate potential problems. We discuss issues addressed methods.

Язык: Английский

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How Do Expectations Shape Perception?

Trends in Cognitive Sciences, Год журнала: 2018, Номер 22(9), С. 764 - 779

Опубликована: Июнь 29, 2018

Язык: Английский

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Communication dynamics in complex brain networks

Nature reviews. Neuroscience, Год журнала: 2017, Номер 19(1), С. 17 - 33

Опубликована: Дек. 14, 2017

Язык: Английский

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Detecting and quantifying causal associations in large nonlinear time series datasets

Science Advances, Год журнала: 2019, Номер 5(11)

Опубликована: Ноя. 1, 2019

A novel causal discovery method for estimating nonlinear interdependency networks from large time series datasets.

Язык: Английский

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Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Frontiers in Neuroscience, Год журнала: 2019, Номер 13

Опубликована: Июнь 6, 2019

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in mid-1990s and attracted increasing attention attempts to discover neural underpinnings cognition neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective among various units. Computational methods, especially graph theory-based have recently played a significant role understanding architecture. Objectives: Thanks emergence theoretical analysis, main purpose current paper is systematically review how properties can emerge through distinct neuronal units cognitive applications fMRI. Moreover, this article provides an overview existing effective methods used construct network, along with their advantages pitfalls. Methods: systematic review, databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, SpringerLink employed for exploring evolution computational 1990 present, focusing on theory. The Cochrane Collaboration's tool was assess risk bias individual studies. Results: Our results show that theory its implications neuroscience researchers since 2009 (as Human Connectome Project launched), because prominent capability characterizing behavior complex systems. Although approach be generally applied either during rest task performance, date, most articles focused resting-state connectivity. Conclusions: This insight into utilize measures make neurobiological inferences regarding mechanisms underlying well different

Язык: Английский

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Could a Neuroscientist Understand a Microprocessor?

PLoS Computational Biology, Год журнала: 2017, Номер 13(1), С. e1005268 - e1005268

Опубликована: Янв. 12, 2017

There is a popular belief in neuroscience that we are primarily data limited, and producing large, multimodal, complex datasets will, with the help of advanced analysis algorithms, lead to fundamental insights into way brain processes information. These do not yet exist, if they did would have no evaluating whether or algorithmically-generated were sufficient even correct. To address this, here take classical microprocessor as model organism, use our ability perform arbitrary experiments on it see methods from can elucidate Microprocessors among those artificial information processing systems both understand at all levels, overall logical flow, via gates, dynamics transistors. We show approaches reveal interesting structure but meaningfully describe hierarchy microprocessor. This suggests current analytic may fall short meaningful understanding neural systems, regardless amount data. Additionally, argue for scientists using non-linear dynamical known ground truth, such validation platform time-series discovery methods.

Язык: Английский

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Granger Causality: A Review and Recent Advances

Annual Review of Statistics and Its Application, Год журнала: 2021, Номер 9(1), С. 289 - 319

Опубликована: Ноя. 18, 2021

Introduced more than a half-century ago, Granger causality has become popular tool for analyzing time series data in many application domains, from economics and finance to genomics neuroscience. Despite this popularity, the validity of framework inferring causal relationships among remained topic continuous debate. Moreover, while original definition was general, limitations computational tools have constrained applications primarily simple bivariate vector autoregressive processes. Starting with review early developments debates, article discusses recent advances that address various shortcomings earlier approaches, models high-dimensional account nonlinear non-Gaussian observations allow subsampled mixed-frequency series.

Язык: Английский

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A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Frontiers in Neuroscience, Год журнала: 2016, Номер 10

Опубликована: Ноя. 10, 2016

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics paradigm design to imaging artifacts, complex protocol definition, multitude processing methods analysis, well intrinsic methodological limitations) must be considered addressed in order optimize fMRI analysis arrive at the most accurate grounded interpretation data. In practice, researcher/clinician choose, many available options, suitable software tool for each stage pipeline. Herein we provide a straightforward guide designed address, major stages, techniques, tools involved process. We developed this help those new technique overcome critical difficulties its use, serve resource neuroimaging community.

Язык: Английский

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Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

NeuroImage Clinical, Год журнала: 2018, Номер 18, С. 849 - 870

Опубликована: Янв. 1, 2018

Biomarkers in whichever modality are tremendously important diagnosing of disease, tracking disease progression and clinical trials. This applies particular for disorders with a long course including pre-symptomatic stages, which only subtle signs can be observed. Magnetic resonance imaging (MRI) biomarkers hold promise due to their relative ease use, cost-effectiveness non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years well established neuroscience related fields. Its increasing application does also include settings therein neurodegenerative diseases. In the present review, we critically summarise state literature on as measured MRI disorders. addition an overview results, briefly outline methods applied concept connectivity. While there many different cumulatively affecting substantial number patients, most them studies fMRI lacking. Plentiful amounts papers available Alzheimer's (AD) Parkinson's (PD), but few works being less allows some conclusions potential acting biomarker aforementioned two diseases, tentative statements others. For AD, contains relatively strong consensus regarding impairment default mode network compared healthy individuals. However, AD is no considerable documentation how that alteration develops longitudinally disease. PD, research points towards alterations mainly limbic motor regions networks, drawing PD has done caution heterogeneity rare clear drawn published results. Nevertheless, summarising data characteristic Huntington's frontotemporal dementia, dementia Lewy bodies, multiple systems atrophy spinocerebellar ataxias. Overall at this point time, promising eventual use biomarker, although remain issues such reproducibility results lack demonstrating longitudinal changes. Improved providing more precise classifications changes sensitive or therapeutic intervention highly desirable, before routine could eventually reality.

Язык: Английский

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Granger causality for state-space models

Physical Review E, Год журнала: 2015, Номер 91(4)

Опубликована: Апрель 23, 2015

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables broad range of complex physical systems. However, it recognized that moving average (MA) component in the data presents serious confound to analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating may be calculated simply and efficiently from parameters state-space (SS) model. Since SS models are equivalent models, estimated fashion is not degraded presence MA component. This particular significance when filtered, downsampled, observed with noise, or subprocess higher dimensional process, since all these operations-commonplace application domains diverse climate science, econometrics, neurosciences-induce show how causality, conditional unconditional, both time frequency domains, directly model solution discrete algebraic Riccati equation. Numerical simulations demonstrate estimators thus derived have greater statistical power smaller bias than AR estimators. also discuss approach facilitates relaxation assumptions linearity, stationarity, homoscedasticity underlying current methods, opening up potentially significant new areas research analysis.

Язык: Английский

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