Post on 14-Mar-2020
Time-resolved functional brain networks:Dynamics of human brain connectivity at rest
Andrew Zalesky
azalesky@unimelb.edu.au
ICON 2014
Background
Resting-state functional connectivity is dynamic
Time
BOLDSignal
Region 1
Region 2Theoretical:
• Breakspear, 2004
• Deco et al, 2008
• Friston, 1997
• Honey et al, 2007
Empirical:
• Chang & Glover, 2010
• Hutchison et al, 2013
• Kitzbichler et al, 2009
• Smith et al, 2012
Background
Resting-state functional connectivity is dynamic
Time
BOLDSignal
Region 1
Region 2Theoretical:
• Breakspear, 2004
• Deco et al, 2008
• Friston, 1997
• Honey et al, 2007
Empirical:
• Chang & Glover, 2010
• Hutchison et al, 2013
• Kitzbichler et al, 2009
• Smith et al, 2012
However , resting-state networks are studied as static networks...
Time-Average
Time
NetworkProperty
Motivation for studying time-resolved brain connectivity
• Test predictions from global workspace theory(Dehaene et al, 2014)
Inattentional Blindness
Wyart & Tallon-Baudry, 2009
Motivation for studying time-resolved brain connectivity
• Better characterize the “resting-state”
50% of participants are asleep for some interval during dataacquisition
Tagliazucchi & Laufs, 2014
Methods
• 10+10 healthy, young adults analysed• 15 mins rsfMRI with 0.72 s TR
Methods
• 10+10 healthy, young adults analysed• 15 mins rsfMRI with 0.72 s TR
Region
Region
Tim
e
Ghosh et al, 2008
Sliding WindowConnectivity estimated usingcorrelation in regionally averagedrsfMRI time series data fallingwithin tapered windows of length60 seconds (83 TRs).
ThresholdingConnectivity matricesthresholded to ensure a fixedconnection density for all timepoints.
Fluctuations in connectivity are coordinated among themost dynamic connections
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HCP 105115
0.01FWER
Cor
rela
tion
Coe
ffici
ent
Tran
sitio
nC
ount
51%
1 min.
data1
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20
40data1
Sample Null Data
data1
data2
Actual Data
Surrogate
Median
Transition
• Null data was stationary, butotherwise matched to statisticalproperties of BOLD (i.e. auto- &cross-spectrum).
Network Efficiency
Low Efficiency (Low Cost) High Efficiency (High Cost)
Bullmore & Sporns, 2012
Sporadic emergence of brief, high efficiency states
Reg
ion
Time 1 min.
Sample Null DataHCP 105115
Simulated rsfMRI Data(Macaque)
HCP 111312
0.40
Sporadic emergence of brief, high efficiency states
Reg
ion
Time 1 min.
Sample Null DataHCP 105115
Simulated rsfMRI Data(Macaque)
HCP 111312
Low Efficiency State
High Efficiency State
0.40
High efficiency states are metabolically costly
• High efficiency states comprise longer connections than lowefficiency states (p < 0.01).
State Average Connection LengthLow Efficiency 67.1 ± 2.1 mmHigh Efficiency 73.0 ± 1.9 mm
High efficiency states are metabolically costly
• High efficiency states comprise longer connections than lowefficiency states (p < 0.01).
State Average Connection LengthLow Efficiency 67.1 ± 2.1 mmHigh Efficiency 73.0 ± 1.9 mm
• Long connections are metabolically costly (Liang et al, 2013).
• Hypothesis: Intermittent periods of high efficiency may be adynamic strategy that has evolved to minimize metabolicrequirements, while maintaining the connectome in a responsivestate.
Network Modules
Bullmore & Sporns, 2012
Dynamic connections interconnect distinct modules,whereas static connections are intra-modular
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400
Somatomotor
Visual
Default Mode
OrbitofrontalLimbic
Static
Dynamic
Test
Sta
tistic
Dynamic connections have time-averaged correlationsnear zero
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Test
Sta
tistic
Time-Averaged Functional Connectivity (Pearson Correlation Coefficient)
HCP 105115 HCP 118932 HCP 111312
r = −0.44, p < 0.001 r = −0.42, p < 0.001 r = −0.55, p < 0.001
• Hypothesis: Modular decomposition algorithms are effectivelydelineating communities of regions that are interconnected bystatic connections.
Summary
Multiple regions briefly increase their topological efficiency,and by inference, their capacity to transfer information.
But these intervals of high efficiency are supported by longanatomical connections and thus likely carry an extrametabolic cost.
Therefore , intermittent periods of high efficiency may be adynamic strategy that has evolved to minimize metabolicrequirements.
Food foraging in animalsEye tracking
Summary
Multiple regions briefly increase their topological efficiency,and by inference, their capacity to transfer information.
But these intervals of high efficiency are supported by longanatomical connections and thus likely carry an extrametabolic cost.
Therefore , intermittent periods of high efficiency may be adynamic strategy that has evolved to minimize metabolicrequirements.
Food foraging in animalsEye tracking
Time-averaged modular decompositions can be explained bythe layout of static and dynamic connections.
Reproducibility
Regional DefinitionsAAL-116
Random-90
Craddock-200
Confound CleanupCompCor
CompCor + Scrubbing
ICA-based X-Noiseifier
Simulated BOLD Data
MacaqueAxonal
Network
Morris-LecarNeural
Dynamics
BalloonWindkessel
BOLD Model
SimulatedBOLDData
No motion or physiological confounds in simulated BOLD
Honey et al, 2009
Further details:Zalesky A, Fornito A, Cocchi L, Gollo L, Breakspear M.Time-resolved functional brain networks reveal dynamics of humanbrain connectivity at rest.Proc Natl Acad Sci U S A. In Press.