The brain connectome collects the complex network architectures, looking at both static and dynamic functional connectivity. The former normally requires stationary signals and connections. However, the human brain activity and connections are most likely time dependent and dynamic, and related to ongoing rhythmic activity. We developed an open-source MATLAB toolbox DynamicBC with user-friendly graphical user interfaces, implementing both dynamic functional and effective connectivity for tracking brain dynamics from functional MRI. We provided two strategies for dynamic analysis: (1) the commonly utilized sliding-window analysis and (2) the flexible least squares based time-varying parameter regression strategy. The toolbox also implements multiple functional measures including seed-to-voxel analysis, region of interest (ROI)-to-ROI analysis, and voxel-to-voxel analysis. We describe the principles of the implemented algorithms, and then present representative results from simulations and empirical data applications. We believe that this toolbox will help neuroscientists and neurologists to easily map dynamic brain connectomics.
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References
1.
Alexander-BlochA, GieddJN, BullmoreE. 2013. Imaging structural co-variance between human brain regions. Nat Rev Neurosci, 14:322–336.
2.
AllenEA, DamarajuE, PlisSM, ErhardtEB, EicheleT, CalhounVD. 2014. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex, 24:663–676.
3.
BassettDS, WymbsNF, PorterMA, MuchaPJ, CarlsonJM, GraftonST. 2011. Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci U S A, 108:7641–7646.
4.
BeckmannCF, DeLucaM, DevlinJT, SmithSM. 2005. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci, 360:1001–1013.
5.
BiswalB, YetkinFZ, HaughtonVM, HydeJS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 34:537–541.
6.
BresslerSL, MenonV. 2010. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci, 14:277–290.
7.
ChangC, GloverGH. 2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage, 50:81–98.
Chao-GanY, Yu-FengZ. 2010. DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI. Front Syst Neurosci, 4:13.
10.
CuiJ, XuL, BresslerSL, DingM, LiangH. 2008. BSMART: a Matlab/C toolbox for analysis of multichannel neural time series. Neural Netw, 21:1094–1104.
11.
de PasqualeF, Della PennaS, SnyderAZ, LewisC, MantiniD, MarzettiL, et al.2010. Temporal dynamics of spontaneous MEG activity in brain networks. Proc Natl Acad Sci U S A, 107:6040–6045.
12.
DecoG, CorbettaM. 2011. The dynamical balance of the brain at rest. Neuroscientist, 17:107–123.
13.
DiX, BiswalBB. 2013. Dynamic brain functional connectivity modulated by resting-state networks. Brain Struct Funct [Epub ahead of print]; DOI: 10.1007/s00429-00013-00634-00423.
14.
FornitoA, HarrisonBJ, ZaleskyA, SimonsJS. 2012. Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proc Natl Acad Sci U S A, 109:12788–12793.
15.
FoxMD, SnyderAZ, VincentJL, CorbettaM, Van EssenDC, RaichleME. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A, 102:9673–9678.
16.
FranssonP. 2005. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum Brain Mapp, 26:15–29.
17.
FristonKJ. 1994. Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp, 2:56–78.
18.
FristonKJ. 2009. Dynamic causal modeling and Granger causality comments on: the identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage, 58:303–305.
19.
FristonKJ. 2011. Functional and effective connectivity: a review. Brain Connect, 1:13–36.
HastieT, TibshiraniR. 1993. Varying-coefficient models. J R Stat Soc Series B Stat Methodol, 55:757–796.
22.
HeB, DaiY, AstolfiL, BabiloniF, YuanH, YangL. 2011. eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. J Neurosci Methods, 195:261–269.
23.
HeY, ChenZJ, EvansAC. 2007. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex, 17:2407–2419.
24.
HutchisonRM, WomelsdorfT, AllenEA, BandettiniPA, CalhounVD, CorbettaM, et al.2013a. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage, 80:360–378.
25.
HutchisonRM, WomelsdorfT, GatiJS, EverlingS, MenonRS. 2013b. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp, 34:2154–2177.
26.
KalabaR, TesfatsionL. 1989. Time-varying linear regression via flexible least squares. Comput Math Appl, 17:1215–1245.
27.
KalmanRE. 1960. A new approach to linear filtering and prediction problems. Basic Eng, 82:35–45.
28.
KangJ, WangL, YanC, WangJ, LiangX, HeY. 2011. Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches. Neuroimage, 56:1222–1234.
29.
LeeHL, ZahneisenB, HuggerT, LeVanP, HenningJ. 2013. Tracking dynamic resting-state networks at higher frequencies using MR-encephalography. Neuroimage, 65:216–222.
30.
LiaoW, ZhangZ, MantiniD, XuQ, JiGJ, ZhangH, et al.2013a. Dynamical intrinsic functional architecture of the brain during absence seizures. Brain Struct Funct [Epub ahead of print]; DOI: 10.1007/s00429-00013-00619-00422.
31.
LiaoW, ZhangZ, MantiniD, XuQ, WangZ, ChenG, et al.2013b. Relationship between large-scale functional and structural covariance networks in idiopathic generalized epilepsy. Brain Connect, 3:240–254.
32.
LiaoW, ZhangZ, PanZ, MantiniD, DingJ, DuanX, et al.2010. Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One, 5:e8525.
33.
LiuX, DuynJH. 2013. Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc Natl Acad Sci U S A, 110:4392–4397.
MarinazzoD, PellicoroM, WuG, AngeliniL, CortesJM, StramagliaS. 2014. Information transfer and criticality in the ising model on the human connectome. PLoS One, 9:e93616.
36.
PengJ-Y, AstonJAD. 2011. The state space models toolbox for MATLAB. J Stat Softw, 41:1–26.
37.
RaichleME, MacLeodAM, SnyderAZ, PowersWJ, GusnardDA, ShulmanGL. 2001. A default mode of brain function. Proc Natl Acad Sci U S A, 98:676–682.
38.
RubinovM, SpornsO. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52:1059–1069.
39.
SongXW, DongZY, LongXY, LiSF, ZuoXN, ZhuCZ, et al.2011. REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One, 6:e25031.
40.
SpornsO. 2011. The non-random brain: efficiency, economy, and complex dynamics. Front Comput Neurosci, 5:5.
41.
SpornsO. 2013. Structure and function of complex brain networks. Dialogues Clin Neurosci, 15:247–262.
42.
SpornsO. 2014. Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci, 17:652–660.
43.
SpornsO, TononiG, KotterR. 2005. The human connectome: a structural description of the human brain. PLoS Comput Biol, 1:e42.
44.
SprengRN, SepulcreJ, TurnerGR, StevensWD, SchacterDL. 2013. Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. J Cogn Neurosci, 25:74–86.
45.
TagliazucchiE, BalenzuelaP, FraimanD, ChialvoDR. 2012a. Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis. Front Physiol, 3:15.
46.
TagliazucchiE, von WegnerF, MorzelewskiA, BrodbeckV, LaufsH. 2012b. Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front Hum Neurosci, 6:339.
47.
TomasiD, VolkowND. 2010. Functional connectivity density mapping. Proc Natl Acad Sci U S A, 107:9885–9890.
48.
van den HeuvelM, MandlR, Hulshoff PolH. 2008. Normalized cut group clustering of resting-state FMRI data. PLoS One, 3:e2001.
49.
Whitfield-GabrieliS, Nieto-CastanonA. 2012. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect, 2:125–141.
50.
WuGR, ChenF, KangD, ZhangX, MarinazzoD, ChenH. 2011. Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain. IEEE Trans Biomed Eng, 58:3088–3096.
51.
WuGR, LiaoW, StramagliaS, DingJR, ChenH, MarinazzoD. 2013a. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med Image Anal, 17:365–374.
52.
WuGR, StramagliaS, ChenH, LiaoW, MarinazzoD. 2013b. Mapping the voxel-wise effective connectome in resting state FMRI. PLoS One, 8:e73670.
53.
XiaM, WangJ, HeY. 2013. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One, 8:e68910.
ZhangZ, LiaoW, ZuoXN, WangZ, YuanC, JiaoQ, et al.2011. Resting-state brain organization revealed by functional covariance networks. PLoS One, 6:e28817.
56.
ZuoXN, EhmkeR, MennesM, ImperatiD, CastellanosFX, SpornsO, et al.2012. Network centrality in the human functional connectome. Cereb Cortex, 22:1862–1875.
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