Methods in Brain Connectivity Inference through Multivariate Time Series Analysis

278 Pages·4.341 MB·English

by  Baccala, Luiz Antonio, Sameshima, Koichi

BIOLOGICAL SCIENCES
Sameshima
Baccalá
Interest in brain connectivity inference has become ubiquitous and is now increasingly M
E METHODS in BRAIN CONNECTIVITY
adopted in experimental investigations of clinical, behavioral, and experimental T
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neurosciences. Methods in Brain Connectivity Inference through Multivariate O
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Time Series Analysis gathers the contributions of leading international authors who S
in INFERENCE THROUGH MULTIVARIATE
discuss different time series analysis approaches, providing a thorough survey of B
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information on how brain areas effectively interact.
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Incorporating multidisciplinary work in applied mathematics, statistics, and animal O
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and human experiments at the forefront of the field, the book addresses the use N
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of time series data in brain connectivity interference studies. Contributors present C
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codes and data examples to back up their methodological descriptions, exploring V
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the details of each proposed method as well as an appreciation of their merits and Y
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limitations. Supplemental material for the book, including code, data, practical N
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examples, and color figures is supplied in the form of a CD with directories organized ER
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by chapter and instruction files that provide additional detail. N
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The field of brain connectivity inference is growing at a fast pace with new data/ H
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signal processing proposals emerging so often as to make it difficult to be fully up O
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to date. This consolidated panorama of data-driven methods includes theoretical G
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bases allied to computational tools, offering readers immediate hands-on experience M
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in this dynamic arena. L
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Contents
SeriesPreface.............................................................vii
Preface...................................................................ix
SoftwareandDataCD ..................................................... xi
Editors..................................................................xiii
Contributors..............................................................xv
Chapter1 BrainConnectivity:AnOverview.....................................1
LuizA.Baccala´ andKoichiSameshima
Section I Fundamental Theory
Chapter2 DirectedTransferFunction:APioneeringConcept
inConnectivityAnalysis..............................................13
MaciejKamin´skiandKatarzynaBlinowska
Chapter3 AnOverviewofVectorAutoregressiveModels .....................35
PedroA.Morettin
Chapter4 PartialDirectedCoherence ...........................................57
LuizA.Baccala´ andKoichiSameshima
Chapter5 InformationPartialDirectedCoherence..............................75
DanielY.Takahashi,LuizA.Baccala´,andKoichiSameshima
Chapter6 AssessingConnectivityinthePresence
ofInstantaneousCausality ............................................87
LucaFaes
Chapter7 AsymptoticPDCProperties..........................................113
KoichiSameshima,DanielY.Takahashi,andLuizA.Baccala´
v
vi Contents
Section II Extensions
Chapter8 NonlinearParametricGrangerCausality
inDynamicalNetworks..............................................135
DanieleMarinazzo,WeiLiao,MarioPellicoro,
andSebastianoStramaglia
Chapter9 Time-VariantEstimationofConnectivityandKalmanFilter ......161
LindaSommerlade,MarcoThiel,BettinaPlatt,AndreaPlano,
GernotRiedel,CelsoGrebogi,WolfgangMader,MalenkaMader,
JensTimmer,andBjörnSchelter
Section III Applications
Chapter10 ConnectivityAnalysisBasedonMultielectrodeEEGInversion
MethodswithandwithoutfMRIAPrioriInformation.............181
LauraAstolfiandFabioBabiloni
Chapter11 MethodsforConnectivityAnalysisinfMRI........................197
JoãoRicardoSato,PhilipJ.A.Dean,andGilsonVieira
Chapter12 AssessingCausalInteractionsamongCardiovascular
VariabilitySeriesthroughaTime-Domain
GrangerCausalityApproach.........................................223
AlbertoPorta,AnielleC.M.Takahashi,AparecidaM.Catai,and
NicoloMontano
Section IV Epilogue
Chapter13 MultivariateTime-SeriesBrainConnectivity:ASum-Up .........245
LuizA.Baccala´ andKoichiSameshima
Index...................................................................253
Series Preface
TheFrontiersinNeuroengineeringseriespresentstheinsightsofexpertsonemerging
experimentaltechniquesandtheoreticalconceptsthatareorwillbeatthevanguardof
neuroscience.Booksintheseriescovertopicsrangingfromelectrodedesignmeth-
ods for neural ensemble recordings in behaving animals to biological sensors. The
series also covers new and exciting multidisciplinary areas of brain research, such
as computational neuroscience and neurengineering, and describes breakthroughs
in biomedical engineering. The goal is for this series to be the reference that every
neuroscientistusestobecomeacquaintedwithnewadvancesinbrainresearch.
Each book is edited by an expert and consists of chapters written by leaders in
aparticularfield.Thebooksarerichlyillustratedandcontaincomprehensivebibli-
ographies.Chaptersprovidesubstantialbackgroundmaterialrelevanttotheparticular
subjects.
We hope that as the volumes become available, our efforts as well as those of
thepublisher,thebookeditors,andtheindividualauthorswillcontributetothefur-
therdevelopmentofbrainresearch.Theextenttowhichweachievethisgoalwillbe
determinedbytheutilityofthesebooks.
SidneyA.Simon,PhD
SeriesEditor
vii

Preface
Editing Methods in Brain Connectivity Inference through Multivariate Time Series
Analysis gave us the chance to reflect on this new and exciting field based on the
exploitation of observational brain data. The field is growing at a fast pace with
newdata/signalprocessingproposalsemergingsooftenastomakeitdifficulttobe
fullyuptodate.Wethereforeoptedtoofferaconsolidatedpanoramaofdata-driven
(as opposed to model-driven) methods with their theoretical basis allied to compu-
tational tools to provide the reader with immediate hands-on experience. Alas, not
allofourcoauthorscouldsupplythosetools,somethingunderstandablesinceeven
good software is frequently hard to employ and often needs to be written for user-
friendliness—analltoospecializedskillinitself.Nevertheless,webelievethatboth
thedataandthesuppliedsoftwaresdoprovideagoodstartingpoint.
This area depends heavily on time-series analysis tools. Some of them were
inspiredandborntoaddressspecificbrainconnectivityproblemsbuttranslateimme-
diatelybackintotime-seriesanalysisandalliedapplicationareas.Thebridgeback,
however, hasnotyetbeencrossedandwehopethatthecurrenttomealsoproves a
helpfulincentivetorevisittime-seriesissues.
Wewouldliketothanktheserieseditorforthefreedomgiventous,andwhichwe
hope, we transferred to our chapter authors—all of them original pioneers—whose
differencesinbothapproachandopinionexposeopenproblemsandthrowlightonto
possiblepathwaysforfurtherresearch.Itshouldbenotedthatmanychapterscontain
brandnewmaterial,therebyaddingvaluetothebook.
Financial support for this endeavor came from FAPESP-CInAPCe Grant
2005/56464-9; CNPq Grants 304404/2009-8 (L.A.B.) and 309381/2012-6 (K.S.);
FFMtoK.S.
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