HHS Public Access Author manuscript Author Manuscript
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30. Published in final edited form as: Int J Med Biol Front. 2015 ; 21(2): 207–218.
Anterior-Posterior Connectivity within the Default Mode Network Increases During Maturation Stuart D. Washington*,1,2 and John W. VanMeter1,2,* 1Center
for Functional and Molecular Imaging, Georgetown University Medical Center Washington, DC, US
Author Manuscript
2Department
of Neurology, Georgetown University Medical Center Washington, DC, US
Abstract
Author Manuscript
The default mode network (DMN) supports self-referential thought processes important for successful socialization including: theory-of-mind, episodic memory, and prospection. Connectivity between DMN's nodes, which are distributed between the frontal, temporal, and parietal lobes, change with age and may continue changing into adulthood. We have previously explored the maturation of functional connections in the DMN as they relate to autism spectrum disorder (ASD) in children 6 to 18 years of age. In this chapter, we refine our earlier study of DMN functional maturation by focusing on the development of inter-nodal connectivity in a larger pool of typically developing people 6 to 25 years of age (mean = 13.22 years ± 5.36 s.d.; N = 36; 42% female). Correlations in BOLD activity (Fisher's Z) between ROIs revealed varying strengths of functional connectivity between regions, the strongest of which was between the left and right inferior parietal lobules or IPLs (Z = 0.62 ± 0.25 s.d.) and the weakest of which was between the posterior cingulate cortex (PCC) and right middle temporal gyrus or MTG (Z = 0.06 ± 0.22 s.d.). Further, connectivity between two pairs of DMN nodes significantly increased as a quadratic function of age (p < 0.05), specifically the anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) and PCC nodes and the left IPL and right MTG nodes. The correlation between ACC/mPFC ↔ PCC connectivity and age was more significant than the correlation between left IPL ↔ right MTG connectivity and age by more than an order of magnitude. We suggest that these changes in functional connectivity in part underlie the introspective mental changes known to commonly occur between the preadolescent and adult years. A range of neurological and psychological conditions that hamper social interactions, from ASD to psychopathy, may be marked by deviations from this maturational trajectory.
Author Manuscript
Keywords default mode network; functional connectivity; development
*
Corresponding author: Stuart D. Washington, PhD and John W. VanMeter, PhD, Room LM14, Preclinical Sciences Building, Georgetown University Medical Center, 3900 Reservoir Rd, NW, Washington, DC 20057, Phone: (202) 687-4076, [email protected] and [email protected]..
Washington and VanMeter
Page 2
Author Manuscript
Introduction
Author Manuscript
Relative to adults, even typically developing children and adolescents are commonly described as impulsive and generally unaware of how others perceive them (Jabr 2011). Scientific research into the psychological and neural development of typical adolescents is thus making an increasingly large impact on social studies, including education (Blakemore 2010) and criminal justice (Scott and Steinberg 2008; Steinberg 2009). The development of self-reflective thought processes, such as envisioning consequences of one's actions, recalling consequences of past actions, and imagining events from the perspectives of others are highly pertinent to socialization. Those capable of anticipating the consequences of their future actions (prospection), autobiographically recalling consequences of their past actions (episodic memory), and imagining events from the perspectives of others (theory of mind) may be more likely to obtain societal rewards (e.g., higher status or salary) and avoid societal penalties (e.g., social ostracism or even incarceration). These self-reflective thought processes typically begin developing during childhood (Addis et al., 2007; Dahlgren et al., 2010; Kim et al., 2010; Lackner et al., 2010; Lombardo et al., 2010; Saxe and Kanwisher 2003; Spreng and Grady 2010).
Author Manuscript
Multiple neural circuits are involved in self-reflective social cognition, including the superior temporal sulcus, temporoparietal junction, and the medial prefrontal cortex (Blakemore 2010). One neural network implicated as a substrate of prospection, episodic memory, and theory of mind is the default mode network or DMN (Addis et al., 2007; Buckner et al., 2008; Greicius and Menon 2004; Kim 2010; Kim et al., 2010; Spreng and Grady 2010). The DMN is comprised of dispersed cortical nodes including the anterior cingulate/medial prefrontal and the posterior cingulate cortex amongst others (Buckner et al., 2008; Greicius et al., 2003; Greicius and Menon 2004; Raichle et al., 2001). The social domain deficits characteristic of autism spectrum disorder (ASD) are related to divergent functional connectivity between DMN nodes (Doyle-Thomas et al., 2015; Lynch et al., 2013; Muller et al., 2011; Washington et al., 2014). Therefore, it stands to reason that functional connectivity between key DMN nodes should change as children mature into adulthood. We assessed functional connectivity between DMN nodes by analyzing task-background functional magnetic resonance imaging (fMRI) data obtained from children, preadolescents, teens, and adults. We then showed how inter-nodal DMN connectivity matures from childhood to adulthood.
Methods Author Manuscript
Subjects A subset of thirty-six healthy, right-handed subjects (15 female) between 6 and 25 years of age (13.22 years ± 5.36 s.d.) were included in this study. Adult subjects provided their consent. Children who participated in the study as subjects provided their assent, and written consent was obtained from each child's parent following explanation of experimental procedures.
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30.
Washington and VanMeter
Page 3
Author Manuscript
Each subject (adults and children) was compensated for his or her participation. All methods used to obtain these data, as well as methods of consent and study protocols, were approved by the Georgetown University Medical Center Internal Review Board (GUMC IRB Protocol Number 2006-475). Data Acquisition Imaging was performed using a 3T whole-body MRI scanner (Siemens Magnetom Trio, Erlangen, Germany) and a circularly polarized head coil. Head movement was minimized by padding that firmly yet comfortably held the subject's head still in the coil.
Author Manuscript
Functional MRI Data—Subjects were scanned during performance of the modified flanker task using an echo-planar imaging (EPI) sequence sensitive to blood oxygenation level dependent (BOLD) signal with the following parameters: TR = 3000ms, TE = 30ms, flip angle = 90°, matrix size = 64×64, FOV = 192×192mm2, 50 slices with a thickness of 2.8mm and a 0.2mm gap for an effective resolution of 3.0×3.0×3.0mm3 for a total run time of 6 minutes, 33 seconds. Structural MRI Data—Neuroanatomical localization of BOLD activity was determined using a structural protocol consisting of a magnetization prepared rapid acquisition gradient echo (MPRAGE) scan acquired during the same scanning session with the following parameters: TR = 1600ms, TE = 4.38ms, TI = 640ms, flip angle = 15°, averages = 1, 160 slices with a 1.0mm thickness, FOV = 256×256mm2, effective resolution = 1.0×1.0×1.0mm3, scan time = 6 minutes, 51 seconds. All structural scans were examined for gross neurological findings.
Author Manuscript
Data Analysis Preprocessing—fMRI scan data were preprocessed using SPM5 (Wellcome Department of Cognitive Neurology, London, UK). The first three volumes (9s) of each functional run were discarded to allow for equilibrium in longitudinal magnetization. The images were corrected for differences in slice acquisition times and then were spatially realigned to the first volume to correct for head motion. The functional images were spatially normalized using the EPI template from SPM5, and smoothed with a Gaussian kernel of 9 mm. Based on an analysis of motion using ArtRepair (Mazaika et al., 2009), subjects who exhibited significant motion artifacts (threshold of 10% of scans per subjects having >1.5% average global whole volume signal change) were excluded from further analysis.
Author Manuscript
Region-of-Interest (ROI) Demarcation—To measure differences in functional connectivity between specific nodes within the default mode network (DMN), we conducted correlations of the signal time courses within specific regions of interest (ROIs). ROIs were based on a set of DMN nodes previously demarcated using independent components analysis (Washington et al., 2014).
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30.
Washington and VanMeter
Page 4
Author Manuscript
Nodes corresponded to the anterior cingulate/medial prefrontal cortex (ACC/mPFC), precuneus/posterior cingulate cortex (PCC), and bilateral (i.e., separate ROIs for the left and right hemispheres) middle temporal gyrus (MTG) and inferior parietal lobule (IPL).
Author Manuscript
Functional Connectivity Analysis—Our functional connectivity data were based on task-background BOLD activity derived from time courses obtained while subjects completed a modified flanker task. This modified flanker task was designed to match figures from the Naglieri Nonverbal Ability Test (Washington et al., 2014). The stimuli in the modified flanker task consisted of presentations of 3×3 arrays of block arrows. Subjects were instructed to respond based on the orientation of the center arrow using a set of button boxes. The task used a block design alternating between fixation and the flanker stimuli with each block lasting 48 seconds. Further details on this task may be found in Washington et al., (2014). We convolved the task time course with the hemodynamic response function (HRF). The resultant function was entered as a regressor of non-interest in our ROI analyses (see above). This approach allowed us to use the entire 6 minute, 33 second time course for a task-background functional connectivity analysis.
Author Manuscript
Excessive framewise displacement (FD) can cause spurious BOLD activity which may be mistakenly associated with functional connectivity (Power et al., 2012). With more continuous resting-state signals, “scrubbing” (i.e., removal of volumes with excessive FD as well as the immediate preceding and succeeding volumes) is a very practical and common approach. We had two reasons for adopting alternatives to scrubbing, however. First, scrubbing potentially leaves different subjects with a differing number of volumes, making them less comparable. Second, even though task was regressed-out of our signals, some volumes were acquired during task while others were not. Those volumes acquired during task underwent regression at a different phase in the convolved (HRF+Task) function than those that were not. Removing volumes with excessive FD from our time course would render our between subjects comparisons less than ideal. As an alternative, we adopted the “spike regression with global signal regression” strategy (Yan et al., 2013). Using this strategy, we identified volumes with excessive FD, as well as their preceding and succeeding volumes, in each subject and modeled each one as a separate regressor.
Author Manuscript
This strategy also necessitated regressing realigned data on voxel-specific head motion parameters for each subject, per the Friston 24-parameter model (Friston et al., 1996). Yan et al., (2013) suggests the additional step of incorporating global signal regression (GSR). Thus, regressors of non-interest based on vectors corresponding to volumes with excessive FD (>0.5 mm), the Friston 24-parameter model, and GSR were included in our ROI analyses. We altogether excluded 15 subjects whose mean FD values exceeded 0.5 mm, reducing an initial subject pool of 51 subjects down to the 36 we ultimately used. The time-courses for each voxel within an ROI were averaged, and the mean residual timecourses within each pair of ROIs were then correlated against each other in a pairwise fashion to assess functional connectivity. This procedure generated a correlation value
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30.
Washington and VanMeter
Page 5
Author Manuscript
(Pearson's r), which was converted to a Fisher's Z, reflecting the strength of functional connectivity between each ROI pair. Effects of Age and Connection-Type on Functional Connectivity Strength—We performed a Repeated Measures Analysis of Variance (ANOVA) to determine how the strength of functional connectivity is affected by age after splitting the subjects into younger and older age groups based on the median age of 12 years. Post-hoc, we searched for second-order correlations between age and functional connectivity by plotting functional connectivity as a function of age for each of the 15 ROI-pairs. We used quadratic fits since they tend to match the trajectory of synaptogenesis (Giedd et al., 1999; Gogtay et al., 2004; Lenroot and Giedd 2006).
Results Author Manuscript
The boundaries of each ROI and the strength of functional connectivity between regionpairs are given in Figures 1A and 1B. A repeated-measures ANOVA revealed that there was a main effect of region-pair (F [14, 476] = 14.94, p < 0.01) on functional connectivity such that connectivity between certain DMN nodes were significantly greater than others. The following five pairs of DMN nodes had the strongest functional connectivity (Fisher's Z), regardless of subject age: left IPL ↔ right IPL (Z=0.62 ± 0.25), ACC/mPFC ↔ left IPL (Z=0.50 ± 0.22), left IPL ↔ right MTG (Z=0.46 ± 0.29), ACC/mPFC ↔ left MTG (Z=0.44 ± 0.20), and ACC/mPFC ↔ PCC (Z=0.43 ± 0.18). There was no main-effect of age-group (F [1, 34] = 1.419, p = 0.24) on functional connectivity. However, there was an interactioneffect of region-pair and age-group (F [14, 476] = 2.83, p = 0.01) such that the strength of connectivity between certain pairs of DMN nodes is age-dependent.
Author Manuscript
Since the ANOVA results showed that age-group interacted with region-pair to affect functional connectivity, we directly examined functional connectivity as a function of age for all 15 ROI pairs. We first noticed that there is no direct association between connectivity strength and developmental trajectory (Pearson's r of the quadratic correlation). Specifically, though the only two region-pairs with connectivity that significantly correlated with age (left IPL ↔ right MTG and ACC/mPFC ↔ PCC) are amongst the five region-pairs with the greatest connectivity, other region-pairs have greater connectivity, as Figure 1B shows. The functional connectivity of ACC/mPFC ↔ PCC correlated far better with age (r = 0.55, p = 6×10−4) than left IPL ↔ right MTG (r = 0.4, p = 0.02). The correlation between ACC/ mPFC ↔ PCC functional connectivity and age, shown in Figure 1C, is strong enough to surpass the significance threshold of a Bonferroni correction (α = 0.05/15 = p = 0.003).
Author Manuscript
Discussion The results presented here demonstrate that functional connectivity between the most anterior (ACC/mPFC) and most posterior (PCC) nodes of the default mode network increase as a quadratic function of age between childhood (>5 years of age) and adulthood (
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30. Published in final edited form as: Int J Med Biol Front. 2015 ; 21(2): 207–218.
Anterior-Posterior Connectivity within the Default Mode Network Increases During Maturation Stuart D. Washington*,1,2 and John W. VanMeter1,2,* 1Center
for Functional and Molecular Imaging, Georgetown University Medical Center Washington, DC, US
Author Manuscript
2Department
of Neurology, Georgetown University Medical Center Washington, DC, US
Abstract
Author Manuscript
The default mode network (DMN) supports self-referential thought processes important for successful socialization including: theory-of-mind, episodic memory, and prospection. Connectivity between DMN's nodes, which are distributed between the frontal, temporal, and parietal lobes, change with age and may continue changing into adulthood. We have previously explored the maturation of functional connections in the DMN as they relate to autism spectrum disorder (ASD) in children 6 to 18 years of age. In this chapter, we refine our earlier study of DMN functional maturation by focusing on the development of inter-nodal connectivity in a larger pool of typically developing people 6 to 25 years of age (mean = 13.22 years ± 5.36 s.d.; N = 36; 42% female). Correlations in BOLD activity (Fisher's Z) between ROIs revealed varying strengths of functional connectivity between regions, the strongest of which was between the left and right inferior parietal lobules or IPLs (Z = 0.62 ± 0.25 s.d.) and the weakest of which was between the posterior cingulate cortex (PCC) and right middle temporal gyrus or MTG (Z = 0.06 ± 0.22 s.d.). Further, connectivity between two pairs of DMN nodes significantly increased as a quadratic function of age (p < 0.05), specifically the anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) and PCC nodes and the left IPL and right MTG nodes. The correlation between ACC/mPFC ↔ PCC connectivity and age was more significant than the correlation between left IPL ↔ right MTG connectivity and age by more than an order of magnitude. We suggest that these changes in functional connectivity in part underlie the introspective mental changes known to commonly occur between the preadolescent and adult years. A range of neurological and psychological conditions that hamper social interactions, from ASD to psychopathy, may be marked by deviations from this maturational trajectory.
Author Manuscript
Keywords default mode network; functional connectivity; development
*
Corresponding author: Stuart D. Washington, PhD and John W. VanMeter, PhD, Room LM14, Preclinical Sciences Building, Georgetown University Medical Center, 3900 Reservoir Rd, NW, Washington, DC 20057, Phone: (202) 687-4076, [email protected] and [email protected]..
Washington and VanMeter
Page 2
Author Manuscript
Introduction
Author Manuscript
Relative to adults, even typically developing children and adolescents are commonly described as impulsive and generally unaware of how others perceive them (Jabr 2011). Scientific research into the psychological and neural development of typical adolescents is thus making an increasingly large impact on social studies, including education (Blakemore 2010) and criminal justice (Scott and Steinberg 2008; Steinberg 2009). The development of self-reflective thought processes, such as envisioning consequences of one's actions, recalling consequences of past actions, and imagining events from the perspectives of others are highly pertinent to socialization. Those capable of anticipating the consequences of their future actions (prospection), autobiographically recalling consequences of their past actions (episodic memory), and imagining events from the perspectives of others (theory of mind) may be more likely to obtain societal rewards (e.g., higher status or salary) and avoid societal penalties (e.g., social ostracism or even incarceration). These self-reflective thought processes typically begin developing during childhood (Addis et al., 2007; Dahlgren et al., 2010; Kim et al., 2010; Lackner et al., 2010; Lombardo et al., 2010; Saxe and Kanwisher 2003; Spreng and Grady 2010).
Author Manuscript
Multiple neural circuits are involved in self-reflective social cognition, including the superior temporal sulcus, temporoparietal junction, and the medial prefrontal cortex (Blakemore 2010). One neural network implicated as a substrate of prospection, episodic memory, and theory of mind is the default mode network or DMN (Addis et al., 2007; Buckner et al., 2008; Greicius and Menon 2004; Kim 2010; Kim et al., 2010; Spreng and Grady 2010). The DMN is comprised of dispersed cortical nodes including the anterior cingulate/medial prefrontal and the posterior cingulate cortex amongst others (Buckner et al., 2008; Greicius et al., 2003; Greicius and Menon 2004; Raichle et al., 2001). The social domain deficits characteristic of autism spectrum disorder (ASD) are related to divergent functional connectivity between DMN nodes (Doyle-Thomas et al., 2015; Lynch et al., 2013; Muller et al., 2011; Washington et al., 2014). Therefore, it stands to reason that functional connectivity between key DMN nodes should change as children mature into adulthood. We assessed functional connectivity between DMN nodes by analyzing task-background functional magnetic resonance imaging (fMRI) data obtained from children, preadolescents, teens, and adults. We then showed how inter-nodal DMN connectivity matures from childhood to adulthood.
Methods Author Manuscript
Subjects A subset of thirty-six healthy, right-handed subjects (15 female) between 6 and 25 years of age (13.22 years ± 5.36 s.d.) were included in this study. Adult subjects provided their consent. Children who participated in the study as subjects provided their assent, and written consent was obtained from each child's parent following explanation of experimental procedures.
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30.
Washington and VanMeter
Page 3
Author Manuscript
Each subject (adults and children) was compensated for his or her participation. All methods used to obtain these data, as well as methods of consent and study protocols, were approved by the Georgetown University Medical Center Internal Review Board (GUMC IRB Protocol Number 2006-475). Data Acquisition Imaging was performed using a 3T whole-body MRI scanner (Siemens Magnetom Trio, Erlangen, Germany) and a circularly polarized head coil. Head movement was minimized by padding that firmly yet comfortably held the subject's head still in the coil.
Author Manuscript
Functional MRI Data—Subjects were scanned during performance of the modified flanker task using an echo-planar imaging (EPI) sequence sensitive to blood oxygenation level dependent (BOLD) signal with the following parameters: TR = 3000ms, TE = 30ms, flip angle = 90°, matrix size = 64×64, FOV = 192×192mm2, 50 slices with a thickness of 2.8mm and a 0.2mm gap for an effective resolution of 3.0×3.0×3.0mm3 for a total run time of 6 minutes, 33 seconds. Structural MRI Data—Neuroanatomical localization of BOLD activity was determined using a structural protocol consisting of a magnetization prepared rapid acquisition gradient echo (MPRAGE) scan acquired during the same scanning session with the following parameters: TR = 1600ms, TE = 4.38ms, TI = 640ms, flip angle = 15°, averages = 1, 160 slices with a 1.0mm thickness, FOV = 256×256mm2, effective resolution = 1.0×1.0×1.0mm3, scan time = 6 minutes, 51 seconds. All structural scans were examined for gross neurological findings.
Author Manuscript
Data Analysis Preprocessing—fMRI scan data were preprocessed using SPM5 (Wellcome Department of Cognitive Neurology, London, UK). The first three volumes (9s) of each functional run were discarded to allow for equilibrium in longitudinal magnetization. The images were corrected for differences in slice acquisition times and then were spatially realigned to the first volume to correct for head motion. The functional images were spatially normalized using the EPI template from SPM5, and smoothed with a Gaussian kernel of 9 mm. Based on an analysis of motion using ArtRepair (Mazaika et al., 2009), subjects who exhibited significant motion artifacts (threshold of 10% of scans per subjects having >1.5% average global whole volume signal change) were excluded from further analysis.
Author Manuscript
Region-of-Interest (ROI) Demarcation—To measure differences in functional connectivity between specific nodes within the default mode network (DMN), we conducted correlations of the signal time courses within specific regions of interest (ROIs). ROIs were based on a set of DMN nodes previously demarcated using independent components analysis (Washington et al., 2014).
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30.
Washington and VanMeter
Page 4
Author Manuscript
Nodes corresponded to the anterior cingulate/medial prefrontal cortex (ACC/mPFC), precuneus/posterior cingulate cortex (PCC), and bilateral (i.e., separate ROIs for the left and right hemispheres) middle temporal gyrus (MTG) and inferior parietal lobule (IPL).
Author Manuscript
Functional Connectivity Analysis—Our functional connectivity data were based on task-background BOLD activity derived from time courses obtained while subjects completed a modified flanker task. This modified flanker task was designed to match figures from the Naglieri Nonverbal Ability Test (Washington et al., 2014). The stimuli in the modified flanker task consisted of presentations of 3×3 arrays of block arrows. Subjects were instructed to respond based on the orientation of the center arrow using a set of button boxes. The task used a block design alternating between fixation and the flanker stimuli with each block lasting 48 seconds. Further details on this task may be found in Washington et al., (2014). We convolved the task time course with the hemodynamic response function (HRF). The resultant function was entered as a regressor of non-interest in our ROI analyses (see above). This approach allowed us to use the entire 6 minute, 33 second time course for a task-background functional connectivity analysis.
Author Manuscript
Excessive framewise displacement (FD) can cause spurious BOLD activity which may be mistakenly associated with functional connectivity (Power et al., 2012). With more continuous resting-state signals, “scrubbing” (i.e., removal of volumes with excessive FD as well as the immediate preceding and succeeding volumes) is a very practical and common approach. We had two reasons for adopting alternatives to scrubbing, however. First, scrubbing potentially leaves different subjects with a differing number of volumes, making them less comparable. Second, even though task was regressed-out of our signals, some volumes were acquired during task while others were not. Those volumes acquired during task underwent regression at a different phase in the convolved (HRF+Task) function than those that were not. Removing volumes with excessive FD from our time course would render our between subjects comparisons less than ideal. As an alternative, we adopted the “spike regression with global signal regression” strategy (Yan et al., 2013). Using this strategy, we identified volumes with excessive FD, as well as their preceding and succeeding volumes, in each subject and modeled each one as a separate regressor.
Author Manuscript
This strategy also necessitated regressing realigned data on voxel-specific head motion parameters for each subject, per the Friston 24-parameter model (Friston et al., 1996). Yan et al., (2013) suggests the additional step of incorporating global signal regression (GSR). Thus, regressors of non-interest based on vectors corresponding to volumes with excessive FD (>0.5 mm), the Friston 24-parameter model, and GSR were included in our ROI analyses. We altogether excluded 15 subjects whose mean FD values exceeded 0.5 mm, reducing an initial subject pool of 51 subjects down to the 36 we ultimately used. The time-courses for each voxel within an ROI were averaged, and the mean residual timecourses within each pair of ROIs were then correlated against each other in a pairwise fashion to assess functional connectivity. This procedure generated a correlation value
Int J Med Biol Front. Author manuscript; available in PMC 2015 July 30.
Washington and VanMeter
Page 5
Author Manuscript
(Pearson's r), which was converted to a Fisher's Z, reflecting the strength of functional connectivity between each ROI pair. Effects of Age and Connection-Type on Functional Connectivity Strength—We performed a Repeated Measures Analysis of Variance (ANOVA) to determine how the strength of functional connectivity is affected by age after splitting the subjects into younger and older age groups based on the median age of 12 years. Post-hoc, we searched for second-order correlations between age and functional connectivity by plotting functional connectivity as a function of age for each of the 15 ROI-pairs. We used quadratic fits since they tend to match the trajectory of synaptogenesis (Giedd et al., 1999; Gogtay et al., 2004; Lenroot and Giedd 2006).
Results Author Manuscript
The boundaries of each ROI and the strength of functional connectivity between regionpairs are given in Figures 1A and 1B. A repeated-measures ANOVA revealed that there was a main effect of region-pair (F [14, 476] = 14.94, p < 0.01) on functional connectivity such that connectivity between certain DMN nodes were significantly greater than others. The following five pairs of DMN nodes had the strongest functional connectivity (Fisher's Z), regardless of subject age: left IPL ↔ right IPL (Z=0.62 ± 0.25), ACC/mPFC ↔ left IPL (Z=0.50 ± 0.22), left IPL ↔ right MTG (Z=0.46 ± 0.29), ACC/mPFC ↔ left MTG (Z=0.44 ± 0.20), and ACC/mPFC ↔ PCC (Z=0.43 ± 0.18). There was no main-effect of age-group (F [1, 34] = 1.419, p = 0.24) on functional connectivity. However, there was an interactioneffect of region-pair and age-group (F [14, 476] = 2.83, p = 0.01) such that the strength of connectivity between certain pairs of DMN nodes is age-dependent.
Author Manuscript
Since the ANOVA results showed that age-group interacted with region-pair to affect functional connectivity, we directly examined functional connectivity as a function of age for all 15 ROI pairs. We first noticed that there is no direct association between connectivity strength and developmental trajectory (Pearson's r of the quadratic correlation). Specifically, though the only two region-pairs with connectivity that significantly correlated with age (left IPL ↔ right MTG and ACC/mPFC ↔ PCC) are amongst the five region-pairs with the greatest connectivity, other region-pairs have greater connectivity, as Figure 1B shows. The functional connectivity of ACC/mPFC ↔ PCC correlated far better with age (r = 0.55, p = 6×10−4) than left IPL ↔ right MTG (r = 0.4, p = 0.02). The correlation between ACC/ mPFC ↔ PCC functional connectivity and age, shown in Figure 1C, is strong enough to surpass the significance threshold of a Bonferroni correction (α = 0.05/15 = p = 0.003).
Author Manuscript
Discussion The results presented here demonstrate that functional connectivity between the most anterior (ACC/mPFC) and most posterior (PCC) nodes of the default mode network increase as a quadratic function of age between childhood (>5 years of age) and adulthood (
