Directional connectivity measures, such as partial directed coherence (PDC), give us means to explore effective connectivity in the human brain. By utilizing independent component analysis (ICA), the original data-set reduction was performed for further PDC analysis.
Purpose
To test this cascaded ICA-PDC approach in causality studies of human functional magnetic resonance imaging (fMRI) data.
Material and Methods
Resting state group data was imaged from 55 subjects using a 1.5 T scanner (TR 1800 ms, 250 volumes). Temporal concatenation group ICA in a probabilistic ICA and further repeatability runs (n = 200) were overtaken. The reduced data-set included the time series presentation of the following nine ICA components: secondary somatosensory cortex, inferior temporal gyrus, intracalcarine cortex, primary auditory cortex, amygdala, putamen and the frontal medial cortex, posterior cingulate cortex and precuneus, comprising the default mode network components. Re-normalized PDC (rPDC) values were computed to determine directional connectivity at the group level at each frequency.
Results
The integrative role was suggested for precuneus while the role of major divergence region may be proposed to primary auditory cortex and amygdala.
Conclusion
This study demonstrates the potential of the cascaded ICA-PDC approach in directional connectivity studies of human fMRI.
SchelterB, WinterhalderM, EichlerMTesting for directed influences among neural signals using partial directed coherence. J Neurosci Methods2006; 152: 210–19.
6.
WinterhalderM, SchelterB, HesseWComparison of linear signal processing techniques to infer directed interactions in multivariate neural systems. Signal Processing2005; 85: 2137–60.
7.
AstolfiL, CincottiF, MattiaDComparison of different cortical connectivity estimators for high-resolution EEG recordings. Hum Brain Mapp2007; 28: 143–57.
8.
SatoJR, TakahashiDY, ArcuriSMFrequency domain connectivity identification: an application of partial directed coherence in fMRI. Hum Brain Mapp2009; 30: 452–61.
9.
HemmelmannD, UngureanuM, HesseWModelling and analysis of time-variant directed interrelations between brain regions based on BOLD-signals. Neuroimage2009; 45: 722–37.
10.
SchelterB, TimmerJ, EichlerM.Assessing the strength of directed influences among neural signals using renormalized partial directed coherence. J Neurosci Methods2009; 179: 121–30.
11.
McKeownMJ, MakeigS, BrownGGAnalysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp1998; 6: 160–88.
12.
KiviniemiV, KantolaJH, JauhiainenJComparison of methods for detecting nondeterministic BOLD fluctuation in fMRI. Magn Reson Imaging2004; 22: 197–203.
13.
JafriMJ, PearlsonGD, StevensMA method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage2008; 39: 1666–81.
14.
CalhounVD, KiehlKA, PearlsonGD.Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum Brain Mapp2008; 29: 828–38.
15.
DemirciO, StevensMC, AndreasenNCInvestigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls. Neuroimage2009; 46: 419–31.
16.
SmithSM, FoxPT, MillerKLCorrespondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci U S A2009; 106: 13040–5.
17.
YlipaavalniemiJ, SaviaE, MalinenSDependencies between stimuli and spatially independent fMRI sources: towards brain correlates of natural stimuli. Neuroimage2009; 48: 176–85.
18.
KiviniemiV, StarckT, RemesJFunctional segmentation of the brain cortex using high model order group PICA. Hum Brain Mapp2009; 30: 3865–86.
19.
HimbergJ, HyvärinenA, EspositoF.Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage2004; 22: 1214–22.
20.
BeckmannCF, SmithSM.Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging2004; 23: 137–52.
21.
Abou-ElseoudA, StarckT, RemesJThe effect of model order selection in group PICA. Hum Brain Mapp2010; 31: 1207–16.
22.
SchlöglA.A comparison of multivariate autoregressive estimators. Signal Processing2006; 86: 2426–9.
23.
SchelterB, WinterhalderM, HellwigBDirect or indirect? Graphical models for neural oscillators. J Physiol Paris2006; 99: 37–46.
24.
DingM, BresslerSL, YangWShort-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern2000; 83: 35–45.
25.
KriegeskorteN, MurM, RuffDAMatching categorical object representations in inferior temporal cortex of man and monkey. Neuron2008; 60: 1126–41.
26.
GrossCG.Single neuron studies of inferior temporal cortex. Neuropsychologia2008; 46: 841–52.
27.
ManiJ, DiehlB, PiaoZEvidence for a basal temporal visual language center: cortical stimulation producing pure alexia. Neurology2008; 71: 1621–7.
28.
NestorA, VettelJM, TarrMJ.Task-specific codes for face recognition: how they shape the neural representation of features for detection and individuation. PLoS One2008; 3:e3978.
29.
HoffmanKL, GhazanfarAA, GauthierICategory-specific responses to faces and objects in primate auditory cortex. Front Syst Neurosci2007; 1: 2.
30.
NäätänenR, PaavilainenP, RinneTThe mismatch negativity (MMN) in basic research of central auditory processing: a review. Clin Neurophysiol2007; 118: 2544–90.
31.
AchardS, SalvadorR, WhitcherBA resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci2006; 26: 63–72.
32.
WuCW, GuH, LuHFrequency specificity of functional connectivity in brain networks. Neuroimage2008; 42: 1047–55.
33.
GrangerCWJ.Investigating causal relations by econometric models and cross-spectral methods. Econometrica1969; 37: 424–38.
34.
GoebelR, RoebroeckA, KimDSInvestigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn Reson Imaging2003; 21: 1251–61.
35.
StevensMC, PearlsonGD, CalhounVD.Changes in the interaction of resting-state neural networks from adolescence to adulthood. Hum Brain Mapp2009; 30: 2356–66.
36.
ZhouZ, DingM, ChenYDetecting directional influence in fMRI connectivity analysis using PCA based Granger causality. Brain Res2009; 1289: 22–9.
37.
LondeiA, D'AusilioA, BassoDBrain network for passive word listening as evaluated with ICA and Granger causality. Brain Res Bull2007; 72: 284–92.
38.
ChangC, ThomasonME, GloverGH.Mapping and correction of vascular hemodynamic latency in the BOLD signal. Neuroimage2008; 43: 90–102.
39.
DavidO, GuillemainI, SailletSIdentifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol2008; 6: 2683–97.
40.
RoebroeckA, FormisanoE, GoebelR.The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution. Neuroimage2009; doi:10.1016/j.neuroimage.2009.09.036.
41.
FristonK.Causal modelling brain connectivity in functional magnetic resonance imaging. PLoS Biol2009; 7:e33.
42.
RamseyJD, HansonSJ, HansonCSix problems for causal inference from fMRI. Neuroimage2010; 49: 1545–58.
43.
LütkepohlH.New introduction to multiple time series analysis.Berlin Heidelberg: Springer; 2005.
44.
KusR, KaminskiM, BlinowskaKJ.Determination of EEG activity propagation: pair-wise versus multichannel estimate. IEEE Trans Biomed Eng2004; 51: 1501–10.
