1. Functional connectivity in fasd
Jeff Wozniak, Ph.D.
Elizabeth Sowell, Ph.D.
Claire Coles, Ph.D.
Julie Kable, Ph.D.
Sara Mattson, Ph.D.

2. Brief overview of functional connectivity
Contrast with task-based fMRI
Avoids challenges of tasks (development, performance, etc.)
Measures global rather than specific (generalizability)
Analysis is potentially more robust / flexible
Network focus
Limited set of biologically meaningful metrics
Examine relations with cognition, clinical variables
Utility in FASD
We know the network ‘backbone’ is abnormal (DTI)
Suited to pediatric populations
Potentially reliable measure of development / intervention

3. Prenatal alcohol exposure is associated with microstructural white matter abnormalities
Ex. DTI tractography shows abnormal white matter tracts with partial agenesis of the corpus callosum
Wozniak, J.R. & Muetzel, R.L. (2011); Neuropsychology Review, 21(2),
Wozniak, J.R., Muetzel, R. M., Mueller, B. A., McGee, C. L., Freerks, M. A., Ward, E. E., Nelson, M. L., Chang, P., & Lim, K. O. (2009). ACER, 33(10),

4. Measurement perspective
Conduction velocity:
Ex. Monkey CC: ave. 7 meters/second
Human brain ≈ 140 mm wide
Travel time = 20 ms
Resting-state fMRI:
Hemodynamic response ≈ 4-6 seconds (4000 – 6000 ms)
Scan is 6 minutes in duration (360,000 ms)
Distribution of axons by diameter
Swadlow & Waxman, 2012 ; McNab, Witzel et al (2012)

5. With fMRI, Our view is from a very high altitude

6. Blackout Scenario:
Identify regions across the country (ex. by shape)
Measure the red given off by taillights
What would this reveal?
Which regions are connected to each other
The capacity of the various connections
The efficiency of various routes (stops required, etc.)

7. Illustration of the relationship between one corpus callosum tract (posterior mid-body) and right & left FreeSurfer pre-central ROIs.

8. Figure 1. fMRI time-series from one control subject illustrating high correlation between BOLD signal change in right and left medial orbital frontal cortex.
Figure 9. fMRI time-series from one FASD subject illustrating low correlation between BOLD signal change in right and left medial orbital frontal cortex.

9. Whole BRAIN functional connectivity analysis
Parcellate the cortext (Freesurfer): 62 Regions of interest (ROIs)
Extract mean fMRI time-series from each ROI
Graph-theory analysis (BCT toolbox); Rubinov & Sporns, 2010
ROIs as nodes, correlation values as connections
Examine costs 0.2 to 0.45 (for today, looking at 0.3)
Characteristic Path Length:
Reflects smallest # of connections to link any two nodes
Measure of serial communication
Higher path length = less efficient long-distance connectivity
Mean Clustering Coefficient:
Density of connections between neighbors
High clustering = disconnected networks
Global Efficiency:
Overall efficiency of network communication
Takes parallel paths into account
Robust measure when networks are disconnected
Local Efficiency:
Reflects regional connectivity

10. OVERALL, NETWORK CONNECTIVITY IS A BALANCE:
Dense connections at the “local” or regional level
&
Strategic long-distance connectivity
High Local efficiency = higher clustering, longer path lengths
Low local efficiency = low clustering, shorter path lengths

11. Basic tool: 6 minute eyes-closed resting fMRI scan
The brain is never truly at rest
Memory consolidation
Preparation for action (pre-frontal cortical activation)
Global Efficiency:
Overall efficiency of network communication
Takes parallel paths into account
Robust measure when networks are disconnected
Mean ± SD
FASD
(n = 24)
Control
(n = 31) %
Difference
Cohen’s d
Effect size
Characteristic Path Length
2.27 ± 0.19
2.20 ± 0.09
3.1%
0.47 **
Mean Clustering Coefficient
0.58 ± 0.05
0.56 ± 0.04
3.5%
0.44 *
Local Efficiency
0.76 ± 0.03
0.75 ± 0.03
1.3%
0.33 *
Global Efficiency
0.52 ± 0.02
0.53 ± 0.01
1.9%
0.63 **

12. Wozniak, Mueller, et al., ACER 2013
Mean ± SD
FASD
(n = 24)
Control
(n = 31) %
Difference
Cohen’s d
Effect size
Characteristic Path Length
2.27 ± 0.19
2.20 ± 0.09
3.1%
0.47 **
Mean Clustering Coefficient
0.58 ± 0.05
0.56 ± 0.04
3.5%
0.44 *
Local Efficiency
0.76 ± 0.03
0.75 ± 0.03
1.3%
0.33 *
Global Efficiency
0.52 ± 0.02
0.53 ± 0.01
1.9%
0.63 **
CIFASD Phase III – MN preliminary data (new sample)
Mean ± SD
FAS + Deferred
(n = 26)
Control
(n = 27) %
Difference
Cohen’s d
Effect size
Characteristic Path Length
2.10 ± 0.12
2.04 ± 0.12
2.9 %
0.50 *
Mean Clustering Coefficient
1.11 ± 0.33
1.10. ± 0.39
0.9%
0.03
Local Efficiency
0.80 ± 0.02
0.79 ± 0.02
1.3%
Global Efficiency
0.62 ± 0.01
0.0%
0.00

13. Among those with FAS or Deferred status (n=53)
Height: non-significant
Weight: non-significant
OFC: non-significant
Smooth philtrum: non-significant

14. Functional Connectivity and Cognitive Functioning
Whole-brain local efficiency and Differential Ability Scale correlations
DAS-II Verbal Score: r = -.13, p = .36
DAS-II Spatial Score: r = -.25, p = .07
Matrix Reasoning: r = -.13, p = .32
Quantitative Reasoning: r = -.09, p = .51
Pattern Construction: r = -.29, p=.04 (a SPATIAL task)

15. Functional Connectivity and Cognitive Functioning
Whole-brain local efficiency and D-KEFS Trail Making
Visual-motor scanning: r = -.25, p = .07
Executive functioning (switching): r = .002, p = .99

16. Preliminary results from the multi-site data
Wozniak, Mueller, et al., ACER 2013
Mean ± SD
FASD
(n = 24)
Control
(n = 31) %
Difference
Cohen’s d
Effect size
Characteristic Path Length
2.27 ± 0.19
2.20 ± 0.09
3.1%
0.47 **
Mean Clustering Coefficient
0.58 ± 0.05
0.56 ± 0.04
3.5%
0.44 *
Local Efficiency
0.76 ± 0.03
0.75 ± 0.03
1.3%
0.33 *
Global Efficiency
0.52 ± 0.02
0.53 ± 0.01
1.9%
0.63 **
CIFASD Phase III – MN preliminary data (new sample)
Mean ± SD
FAS + Deferred
(n = 26)
Control
(n = 27) %
Difference
Cohen’s d
Effect size
Characteristic Path Length
2.10 ± 0.12
2.04 ± 0.12
2.9 %
0.50 *
Mean Clustering Coefficient
1.11 ± 0.33
1.10. ± 0.39
0.9%
0.03
Local Efficiency
0.80 ± 0.02
0.79 ± 0.02
1.3%
Global Efficiency
0.62 ± 0.01
0.0%
0.00
CIFASD Phase III – Preliminary data from multiple sites
Mean ± SD
FAS + Deferred
(n = 66)
Control
(n = 57) %
Difference
Cohen’s d
Effect size
Characteristic Path Length
----- ± -----
----- %
------
Mean Clustering Coefficient
------%
Local Efficiency
0.80 ± 0.02
0.79 ± 0.02
1.3%
0.50 *
Global Efficiency
0.62 ± 0.01
0.0%
0.00

17. Preliminary results from the multi-site data
N=123 (14 FAS; 57 Non-FAS; 52 Deferred)

18. Local Efficiency by initial recruitment group

19. Preliminary results from the multi-site data
N=123 (92 PFL > 10%ile; 31 PFL < 10%ile)

20. In progress
Continue to develop methods for “calibrating” data across sites for combining
Test limits on data quality necessary for analyses
Initially began with very high threshold for quality
Important to be able to relax these criteria & include more data
Make summary measures available to CIFASD collaborators
Investigate relationships with cognitive functioning
Examine utility of global connectivity measures as group discriminators
Examine relationships between genes and global connectivity

21. Future (CIFASD IV) Could global measures of network integrity:
1. act as indices of disease severity?
2. be incorporated into diagnosis?
3. be utilized as measures of developmental progression?
4. serve as outcome measures in intervention trials?