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‎plugins/SIFT.wiki/Chapter-1.-Downloads.md

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---
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layout: default
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title: Chapter-1.-Downloads
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long_title: Chapter-1.-Downloads
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parent: SIFT
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grand_parent: Plugins
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---
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## SIFT Downloads
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SIFT releases can be downloaded below
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| You can install SIFT using the [EEGLAB Extension Manager](https://eeglab.org/others/EEGLAB_Extensions.html) (see also section 5.1). |
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|------------------------------------------------------------------------------------------------------------------------|
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<table>
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<tbody>
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<tr class="even">
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<td><p><img src="images/Dl_ico.png"></p></td>
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<td><p><a href="https://sccn.ucsd.edu/eeglab/download/SIFT_SampleData.zip">Sample data for the tutorial</a> (143 Mb)</p></td>
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</tr>
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<tr class="odd">
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<td><p><img src="images/Dlpdf.jpeg"></p></td>
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<td><p>70-page <a href="https://sccn.ucsd.edu/githubwiki/files/sift_manual_0.1a.pdf" title="wikilink">SIFT manual</a>. It gives both SIFT methods theory and a practical guide to using SIFT using downloadable sample data (some of the content in the PDF document is outdated, and this wiki is more up-to-date).
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</tr>
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<tr class="even">
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<td><p><img src="images/Dlpdf.jpeg"></p></td>
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<td><p>Sample slides from the 15th International EEGLAB Workshop in Beijing, China (June 16, 2012): <a href="https://sccn.ucsd.edu/githubwiki/files/sift_lecture.pdf" title="wikilink">SIFT Lecture: Theory and Applications</a> and <a href="https://sccn.ucsd.edu/githubwiki/files/sift_practicum.pdf" title="wikilink">SIFT Lecture: Practicum</a></p></td>
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</tr>
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<tr class="even">
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<td><p><img src="images/Dlpdf.jpeg"></p></td>
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<td><p>A short video lecture on the (very) basic theory and application of SIFT to modeling distributed brain dynamics in EEG is available
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<a href="https://www.youtube.com/watch?v=6_WW6EMHmWo&list=PLXc9qfVbMMN2xFa3w5ceJB52Dx-3Sgg2Z&index=12l">here</a>. A longer video is available <a href="https://youtu.be/NO3hbYlqNF0">here</a>. </td>
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</tr>
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</tbody>
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</table>
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## Additional ressources
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* SIFT has an entire module to simulate data that is not described in this tutorial. This [page](https://sccn.ucsd.edu/wiki/How_to_run_SIFT_simulation) describes it (backup PDF available [here](https://github.com/sccn/SIFT/files/12446930/SIFT_simulation.pdf)). The SIFT simulator allows you to simulate point-like sources, sources on the cortical surface, or sources in fuzzy regions of the brain.
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* SIFT has a folder with scripts ([SIFT/scripts](https://github.com/sccn/SIFT/tree/master/scripts) folder), which contains examples with detailed comments on how to process datasets. It is worth checking out.
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* [Group SIFT](https://github.com/sccn/groupSIFT) is a plugin for generating group-level analysis with SIFT. The plugin has useful recommendations for using SIFT.
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## Citing SIFT
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If you use SIFT for a paper or talk PLEASE don't forget to mention you used SIFT (provide the URL to this wiki) and include the following citation(s):
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- Mullen, T. R. The dynamic brain: Modeling neural dynamics and interactions from human electrophysiological recordings (Order No. 3639187). 2014. Available from [Dissertations &amp; Theses @ University of California](https://escholarship.org/uc/item/7kk2c4nd); ProQuest Dissertations &amp; Theses A&amp;I. (1619637939)
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- Delorme, A., Mullen, T., Kothe, C., et al. "EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing", <em>Computational Intelligence and Neuroscience</em>, vol. 2011, Article ID 130714, 12 pages, 2011, <a href="http://www.hindawi.com/journals/cin/2011/130714/">PDF</a>.
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We are very happy to hear of any papers you have published using this toolbox, especially the ones that make the data and script available to researchers.

‎plugins/SIFT.wiki/Chapter-2.-Introduction.md

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---
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layout: default
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title: Chapter-2.-Introduction
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long_title: Chapter-2.-Introduction
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parent: SIFT
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grand_parent: Plugins
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---
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Mapping the structural and active functional properties of brain
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networks is a key goal of basic and clinical neuroscience and medicine.
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The novelty and importance of this transformative research was recently
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emphasized by the U.S. National Institute of Health in their 2010
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announcement for the Human Connectome Project:
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> *Knowledge of human brain connectivity will transform human
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> neuroscience by providing not only a qualitatively novel class of
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> data, but also by providing the basic framework necessary to
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> synthesize diverse data and, ultimately, elucidate how our brains work
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> in health, illness, youth, and old age.*
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The study of human brain connectivity generally falls under one or more
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of three categories: *structural*, *functional*, and *effective*
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(Bullmore and Sporns, 2009).
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*Structural connectivity* denotes networks of anatomical (e.g., axonal)
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links. Here the primary goal is to understand what brain structures are
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*capable* of influencing each other via direct or indirect axonal
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connections. This might be studied *in vivo* using invasive axonal
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labeling techniques or noninvasive MRI-based diffusion weighted imaging
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(DWI/DTI) methods.
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*Functional connectivity* denotes (symmetrical) correlations in activity
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between brain regions during information processing. Here the primary
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goal is to understand what regions are functionally related through
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correlations in their activity, as measured by some imaging technique. A
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popular form of functional connectivity analysis using functional
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magnetic resonance imaging (fMRI) has been to compute the pairwise
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correlation (or partial correlation) in BOLD activity for a large number
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of voxels or regions of interest within the brain volume.
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In contrast to the symmetric nature of functional connectivity,
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*effective connectivity* denotes asymmetric or causal dependencies
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between brain regions. Here the primary goal is to identify which brain
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structures in a functional network are (causally) influencing other
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elements of the network during some stage or form of information
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processing. Often the term “information flow” is used to indicate
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directionally specific (although not necessarily causal) effective
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connectivity between neuronal structures. Popular effective connectivity
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methods, applied to fMRI and/or electrophysiological (EEG, iEEG, MEG)
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imaging data, include dynamic causal modeling, structural equation
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modeling, transfer entropy, and Granger-causal methods.
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Contemporary research on building a human ‘connectome’ (complete map of
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human brain connectivity) has typically focused on structural
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connectivity using MRI and diffusion-weighted imaging (DWI) and/or on
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functional connectivity using fMRI. However, the brain is a highly
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dynamic system, with networks constantly adapting and responding to
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environmental influences so as to best suit the needs of the individual.
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A complete description of the human connectome necessarily requires
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accurate mapping and modeling of transient directed information flow or
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causal dynamics within distributed anatomical networks. Efforts to
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examine transient dynamics of effective connectivity (causality or
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directed information flow) using fMRI are complicated by low temporal
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resolution, assumptions regarding the spatial stationarity of the
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hemodynamic response, and smoothing transforms introduced in standard
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fMRI signal processing (Deshpande et al., 2009a; Deshpande et al.,
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2009b). While electro- and magneto-encephalography (EEG/MEG) affords
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high temporal resolution, the traditional approach of estimating
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connectivity between EEG electrode channels (or MEG sensors) suffers
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from a high risk of false positives from volume conduction and non-brain
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artifacts. Furthermore, severe limitations in spatial resolution when
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using surface sensors further limits the physiological interpretability
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of observed connectivity. Although precisely identifying the anatomical
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locations of sources of observed electrical activity (the inverse
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problem) is mathematically ill-posed, recent improvements in source
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separation and localization techniques may allow approximate
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identification of such anatomical coordinates with sufficient accuracy
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to yield anatomical insight invaluable to a wide range of cognitive
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neuroscience and neuroengineering applications (Michel et al., 2004). In
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limited circumstances it is also possible to obtain human
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intracranially-recorded EEG (ICE, ECoG, iEEG) that, although highly
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invasive, affords high spatiotemporal resolution and (often) reduced
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susceptibility to non-brain artifacts.
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Once activity in specific brain areas have been identified using source
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separation and localization, it is possible to look for transient
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changes in dependence between these different brain source processes.
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Advanced methods for non-invasively detecting and modeling distributed
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network events contained in high-density EEG data are highly desirable
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for basic and clinical studies of distributed brain activity supporting
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behavior and experience. In recent years, Granger Causality (GC) and its
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extensions have increasingly been used to explore ‘effective’
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connectivity (directed information flow, or causality) in the brain
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based on analysis of prediction errors of autoregressive models fit to
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channel (or source) waveforms. GC has enjoyed substantial recent success
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in the neuroscience community, with over 1200 citations in the last
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decade (Google Scholar). This is in part due to the relative simplicity
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and interpretability of GC – it is a data-driven approach based on
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linear regressive models requiring only a few basic *a priori*
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assumptions regarding the generating statistics of the data. However, it
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is also a powerful technique for system identification and causal
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analysis. While many landmark studies have applied GC to invasively
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recorded local field potentials and spike trains, a growing number of
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studies have successfully applied GC to non-invasively recorded human
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EEG and MEG data (as reviewed in (Bressler and Seth, 2010)). Application
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of these methods in the EEG source domain is also being seen in an
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increasing number of studies (Hui and Leahy, 2006; Supp et al., 2007;
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Astolfi et al., 2007; Haufe et al., 2010).
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In the last decade an increasing number of effective connectivity
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measures, closely related to Granger’s definition of causality, have
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been proposed. Like classic GC, these measures can be derived from
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(multivariate) autoregressive models fit to observed data time-series.
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These measures can describe different aspects of network dynamics and
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thus comprise a complementary set of tools for effective connectivity or
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causal analysis.
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Several toolboxes affording various forms of Granger-causal (or related)
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connectivity analysis are currently available in full or beta-release.
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Table 1 provides a list of several of these toolboxes, along with the
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website, release version, and license. Although these toolboxes provide
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a number of well-written and useful functions, most lack integration
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within a more comprehensive framework for EEG signal processing (the
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exceptions being Fieldtrip's routines, and TSA, which integrates into
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the Biosig EEG/MEG processing suite). Furthermore, many of these may
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implement only one or two (often bivariate) connectivity measures, lack
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tools for sophisticated visualization, or lack robust statistics or
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multi-subject (group) analysis. Finally, to our knowledge, with the
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exception of E-Connectome, none of these toolboxes directly support
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analysis and visualization of connectivity in the EEG source domain.
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These are all factors that our Source Information Flow Toolbox (SIFT),
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combined with the EEGLAB software suite, hopes to address.
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Table caption. A list of free Matlab-based toolboxes for granger-causal
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connectivity analysis in neural data.
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| | | | |
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|------------------------------------------------------------------|------------------|---------|-------------------------------------------------------------|
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| <b>Toolbox Name</b> | <b>Primary Author</b> | <b>Website</b> | <b>License</b> |
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| Granger Causality Connectivity Analysis (GCCA) Toolbox | Anil Seth | <https://www.sussex.ac.uk/research/centres/sussex-centre-for-consciousness-science/resources/connectivity> | GPL 3 |
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| Time-Series Analysis (TSA) Toolbox | Alois Schloegl | <https://sourceforge.net/p/octave/tsa/ci/default/tree/> | GPL 2 |
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| E-Connectome | Bin He | <https://www.nitrc.org/projects/econnectome> | GPL 3 |
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| Fieldtrip | Robert Oosteveld | <http://fieldtrip.fcdonders.nl/> | GPL 2 |
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| Brain-System for Multivariate AutoRegressive Timeseries (BSMART) | Jie Cui | <http://www.brain-smart.org/> | -- |
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SIFT is an open-source Matlab (The Mathworks, Inc.) toolbox for analysis
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and visualization of multivariate information flow and causality,
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primarily in EEG/iEEG/MEG datasets following source separation and
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localization. The toolbox supports both command-line (scripting) and
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graphical user interface (GUI) interaction and is integrated into the
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widely used open-source EEGLAB software environment for
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electrophysiological data analysis (sccn.ucsd.edu/eeglab). There are
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currently four modules: data preprocessing, model fitting and
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connectivity estimation, statistical analysis, and visualization. First methods implemented include a large number of
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popular frequency-domain granger-causal and coherence measures, obtained
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from adaptive multivariate autoregressive models, surrogate and analytic
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statistics, and a suite of tools for interactive visualization of
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information flow dynamics across time, frequency, and (standard or
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personal MRI co-registered) anatomical source locations.
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In this tutorial, we will outline the theory underlying multivariate
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autoregressive modeling and granger-causal analysis. Practical
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considerations, such as data length, parameter selection, and
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non-stationarities are addressed throughout the text and useful tests
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for estimating statistical significance are outlined. This theory
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section is followed by a hands-on walkthrough of the use of the SIFT
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toolbox for analyzing source information flow dynamics in an EEG
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dataset. Here, we will walk through a typical data-processing pipeline
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culminating with the demonstration of some of SIFT’s powerful tools for
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interactive visualization of time- and frequency-dependent directed
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information flow between localized EEG sources in an
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anatomically-coregistered 3D space. Theory boxes throughout the chapter
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will provide additional insight into various aspects of model fitting and
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parameter selection.
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‎plugins/SIFT.wiki/Chapter-3-and-4-Theory.md

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---
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layout: default
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title: Chapter-3-and-4-Theory
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long_title: Chapter-3-and-4-Theory
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parent: SIFT
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grand_parent: Plugins
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---
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The equation of these chapters could not be rendered in the wiki. You may read these chapters in this [PDF](https://github.com/sccn/SIFT/files/12457211/sift_manual_chapter3and4.pdf) document (some of the content in the [full PDF document](https://sccn.ucsd.edu/githubwiki/files/sift_manual_0.1a.pdf) is outdated, and this wiki is more up-to-date concerning the other chapters).

‎plugins/SIFT.wiki/Chapter-5.-Computing-connectivity.md

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---
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layout: default
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title: Chapter-5.-Computing-connectivity
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long_title: Chapter-5.-Computing-connectivity
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parent: SIFT
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grand_parent: Plugins
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---
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This section provides a demonstration of the use of SIFT to estimate and
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visualize source-domain information flow dynamics in an EEG dataset. To
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get the most of this tutorial you may want to download the toolbox and
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sample data and follow along with the step-by-step instructions. The
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toolbox is demonstrated through hands-on examples primarily using SIFT’s
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Graphical User Interface (GUI). Theory boxes provide additional
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information and suggestions at some stages of the SIFT pipeline.
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In order to make the most use of SIFT’s functionality, it is important
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to first separate your data into sources – e.g. using EEGLAB’s built-in
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Independent Component Analysis (ICA) routines. To make use of the
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advanced network visualization tools, these sources should also be
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localized in 3D space e.g. using dipole fitting (**`pop_dipfit()`**).
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Detailed information on performing an ICA decomposition and source
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localization can be found in the EEGLAB wiki. In this example we will be
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using two datasets from a single subject performing a [two-back with
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feedback continuous performance task](https://sccn.ucsd.edu/eeglab/download/SIFT_SampleData.zip) depicted in the figure below (Onton and
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Makeig, 2007). Here the subject is presented with a continuous stream of
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letters, separated by \~1500 ms, and instructed to press a button with
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the right thumb if the current letter matches the one presented twice
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earlier in the sequence and press with the left thumb if the letter is
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not a match. Correct and erroneous responses are followed by an auditory
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“beep” or “boop” sound. Data is collected using a 64-channel Biosemi
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system with a sampling rate of 256 Hz. The data is common-average
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re-referenced and zero-phase high-pass filtered at 0.1 Hz. The datasets
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we are analyzing are segregated into correct (RespCorr) and incorrect
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(RespWrong) responses, time-locked to the button press and separated
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into maximally independent components using Infomax ICA (Bell and
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Sejnowski, 1995). These sources are localized using a single or
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dual-symmetric equivalent-current dipole model using a four-shell
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spherical head model co-registered to the subjects’ electrode locations
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by warping the electrode locations to the model head sphere using tools
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from the EEGLAB dipfit plug-in.
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![Image:images/SIFTfig3.jpg](images/SIFTfig3.jpg )
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*Figure caption. Two-back with feedback CPT (Onton and
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Makeig, 2007).*
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In this exercise, we will be analyzing the information flow between several
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of these anatomically localized sources of brain activity during correct
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responses and error commission.
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The SIFT
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sub-menu options correspond to SIFT’s five main modules: Pre-Processing,
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Model Fitting and Validation, Connectivity Analysis, Statistics, and
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Visualization.
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![Image:images/SIFTfig7.jpg](images/SIFTfig7.jpg )
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*Figure caption. SIFT Data processing pipeline*

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