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

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

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), 133-147. 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), 1825-1835.

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)

With fMRI, Our view is from a very high altitude

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.)

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

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.

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

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

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 **

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

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

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)

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

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

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

Local Efficiency by initial recruitment group

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

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

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?