Frontostriatal maturation predicts cognitive control failure to appetitive cues in adolescents (2011)

Experimental Task

Participants completed a go-nogo task (Hare, et al., 2005; Hare, et al., 2008) with fearful, happy, and calm facial expressions serving as stimuli. The current report focuses on the happy and calm conditions and omits the fear condition from group analyses, which was the focus of a prior report (Hare et al., 2008). Within a single fMRI run, two expression types were presented, one as a `go’ (i.e., target) stimulus to which participants were instructed to press a button, and the other expression serving as a `nogo’ (i.e., nontarget) stimulus for which participants should withhold a button press. All combinations of expressions were used as both targets and nontargets resulting in a 2 (response: go, nogo) by 3 (emotion: fear, calm, happy) factorial design. Prior to the onset of each run, a screen appeared indicating which expression served as the target stimulus, instructing participants to respond to that expression and no other expression. Participants were also instructed to respond as fast as possible but to try to avoid making errors.

Stimuli and apparatus

Stimuli consisted of happy, fearful and calm faces of unique identities from the NimStim set of facial expressions (Tottenham, et al., 2009). Calm faces (mildly pleasant versions of neutral faces) were used because prior work has indicated that neutral faces can be construed as negative in developmental populations (Gross & Ballif, 1991; Herba & Phillips, 2004; Thomas, et al., 2001). The task was presented using EPrime software, viewable by subjects on an overhead liquid crystal display (LCD) panel integrated with the IFIS-SA system (fMRI Devices Corporation, Waukesha, WI). EPrime software, integrated with the IFIS system, logged button responses and reaction times.

Task parameters

Data were acquired in six functional runs representing each combination of emotion (happy, calm, fear) and response (go, nogo; Figure 1) using a rapid event-related design. For each trial, a face appeared for 500 milliseconds followed by a jittered intertrial interval ranging from 2 to 14.5 seconds in duration (mean 5.2 seconds) during which participants rested while viewing a fixation crosshair. A total of 48 trials were presented per run in pseudorandomized order (36 go, 12 nogo). In total, 24 nogo trials and 72 go trials were acquired for each expression type.

Figure 1

Schematic of four trials within an fMRI run. In this example, calm faces are the target stimuli, for which participants should `go’ by pressing a button. Happy faces are the nontarget (`nogo’) stimulus, to which participants should withhold a button press. …

Image Acquisition

Participants were scanned with a General Electric Signa 3.0T fMRI scanner (General Electric Medical Systems, Milwaukee, WI) with a quadrature head coil. A high-resolution, T1 weighted anatomical scan spoiled gradient sequence ([SPGR] 256 × 256 in-plane resolution, 240-mm field of view [FOV], 124 × 1.5-mm axial slices), or a 3D magnetization prepared rapid acquisition gradient echo sequence ([MPRAGE] 256 × 256 in-plane resolution, 240-mm FOV; 124 × 1.5-mm sagittal slices) was acquired for each subject for transformation and localization of data to Talairach grid space. A spiral in and out sequence (Glover & Thomason, 2004) was used to acquire functional data (repetition time = 2500ms, echo time = 30, FOV = 200 mm, Flip angle = 90, skip 0, 64 × 64 matrix). Thirty-four 4-mm-thick coronal slices were acquired per TR a resolution of 3.125 × 3.125 mm covering the entire brain except for the posterior portion of the occipital lobe.

Analysis of behavioral data

Behavioral data were analyzed for accuracy by calculating hit (correct response), miss (incorrect lack of response), correct rejection (correct withholding of response), and false alarm (incorrect response) rates for happy and calm conditions. For analysis purposes, participants were grouped into child (aged 6–12), teen (aged 13–17) and adult (18 years or older) subgroups.

Analysis of fMRI data

FMRI data analysis was performed within Analysis of Functional Neuroimages (AFNI) software (Cox, 1996). Functional data were slice-time corrected, realigned within and across runs to correct for head movement, coregistered with each participant’s high resolution anatomical scan, scaled to percent signal change units, and smoothed with a 6 mm full-width at half-maximum (FWHM) Gaussian kernel.

For each participant, a general linear model analysis was performed to characterize task effects by incorporating task regressors of interest (calm-go, calm-nogo, happy-go, happy-nogo, fear-go, fear-nogo, errors) convolved with a gamma-variate hemodynamic response function, and covariates of non-interest (motion parameters, linear and quadratic trend for each run). For completeness, fear trials were modeled as task regressors (convolved with a canonical gamma-variate hemodynamic response function) but were not analyzed further. Parameter estimate (β) maps representing task effects were then transformed into the standard coordinate space of Talairach and Tournoux (1988) by applying the warping parameters obtained from the transformation of each subject’s high resolution anatomical scan. Talairach transformed parameter estimate maps were resampled to a resolution of 3 × 3 × 3mm.

Random effects group analyses were performed to identify functional regions of interest (ROIs) for subsequent analysis. Specifically, the conditions happy-go, happy-nogo, calm-go, and calm-nogo were carried to a 2 × 2 × 3 group linear mixed effects model with factors of emotion (within-subjects: happy, calm), response (within-subjects: go, nogo), and age (between-subjects: child, teen, adult). The main effect of response map identified candidate regions differentially engaged as a function of cognitive control demands including the right inferior frontal gyrus (x = 32, y = 23, z = 3). Responses modulated by development were identified in the main effect of age map, including a cluster in the ventral striatum (x = −4, y = 11, z = −9).

Imaging findings considered statistically significant exceeded whole-brain correction for multiple comparisons to preserve an alpha < 0.05 by using a p-value/cluster size combination stipulated by Monte Carlo simulations run in the Alphasim program within AFNI. The single exception to whole-brain thresholding was in the analysis of age effects. Given the role of the striatum in the development of impulse control (Vaidya et al., 1998; Casey et al., 2000; Luna et al., 2001; Durston, Thomas, Yang, et al., 2002, Galvan et al., 2006; Somerville & Casey, 2010) it was treated as an a priori region of interest for voxelwise analysis of age effects. Specifically, age effects were queried for within an inclusive anatomical mask containing voxels in the dorsal and ventral striatum, with p < 0.05, corrected statistical thresholding applied based on the striatum search volume (1,060 voxels). For clarity, we refer to thresholding of the age effect data as p < 0.05 small volume corrected (svc) throughout the manuscript.

Regions of interest were created as spheres with a 4mm radius centered about the peaks listed above, each containing ten 3×3×3 voxels. Parameter estimates were extracted for the 4 conditions (happy-go, happy-nogo, calm-go, calm-nogo) for each participant and ROI and were submitted to offline analyses to determine the directionality of effects. Response, emotion, and developmental effects (independent of the voxelwise contrast with which the ROI was defined) were evaluated using 2 (emotion: calm, happy) × 2 (task: go, nogo) × 3 (age: child, teen, adult) ANOVAs. Offline analyses were conducted in SPSS Statistics 17.0 software (SPSS, Chicago, IL).

Significant effects were tested for performance modulation by submitting parameter estimates to bivariate correlations against subjects’ mean false alarm rates. Significant performance effects were followed up with partial correlation analyses to test whether performance effects remained significant when controlling for age. Conversely, significant age effects were followed up with partial correlation analyses to identify whether age effects remained significant when controlling for performance.

Prior work with the go-nogo paradigm has established a role for frontostriatal circuitry in supporting successful behavioral inhibition (Casey et al., 2000; Durston, Thomas, Yang, et al., 2002; Hare et al., 2005). To identify this circuitry in the current dataset, a psychophysiological interaction analysis (PPI) was employed that was sensitive to differential task-based functional connectivity with a seed region in the right inferior frontal gyrus, for which regional activity predicted performance differences across ages. Specifically, this analysis was sensitive to brain regions showing greater functional coupling with the right IFG for correct nogo trials relative to go trials. The PPI analysis was carried out using standard processing steps (Friston, et al., 1997) by extracting the functional timecourse within the seed region (right IFG ROI described above x = 32, y = 23, z = 3), removing sources of noise and artifact, deconvolving the neural signal, and convolving the timecourse data with no go versus go task timings and the canonical hemodynamic response function (as specified in Gitelman, Penny, Ashburner, & Friston, 2003). Group results including all participants, thresholded at p < 0.05, corrected for multiple comparisons at the whole-brain level, identified a single cluster showing significantly greater functional connectivity with the right IFG during nogo than to go trials. This cluster extended medial and posterior from the right IFG to the dorsal striatum specifically to the caudate. A dorsal striatum region of interest was generated based on the connectivity map by centering a 4mm sphere about the cluster sub-peak within the anatomical boundaries of the dorsal striatum (x = 9, y = 13, z = 6).

Signal change values were extracted from this ROI and tested for between-subject coactivation with the ventral striatum and right IFG. Specifically, ventral striatal, dorsal striatal and right IFG signal change values from the ROIs previously described were extracted for the happy-nogo versus happy-go contrast. These values were then submitted to between subjects bivariate correlations within child, teen and adult participant groups. These analyses identify the degree of coactivation across subjects for nogo relative to go trials between these regions within each age group. Identified coactivation values represent the extent to which the tendency to activate one region predicts activation in another region across participants.

fMRI results

Responses modulated by development were identified in the main effect of age map, including a cluster in the ventral striatum (x = −4, y = 11, z = −9; p < 0.05 svc; Figure 3A). Post-hoc analysis of the age main effect showed that adolescents engaged the ventral striatum significantly more than children and adults to happy faces (p’s =/< 0.01; Figure 3B) and to a lesser extent, to calm faces (p’s =/< 0.06; means +/− standard deviation of percent signal change for calm versus rest: children: −0.095 +/− 0.21; teens: 0.046 +/− 0.16; adults: −0.051 +/− 0.17). Analysis of the best-fitting function that represents responding across ages to happy faces showed that a quadratic (inverted U) function explained a significant portion of variance in response to happy faces (F(1,59) = 10.05, p < 0.003) whereas a linear function did not (F(1,59) = 0.54, p > 0.4). The nonlinear enhancement in recruitment in teens remained significant when controlling for differences in task performance (false alarm rate; F(2,59) = 6.77, p < 0.002) and in the control analysis with matched numbers of trials (F(2,59) = 7.80, p = 0.007). The magnitude of activity to happy trials, calm trials, and no-go versus go trials was not associated with task performance (p’s > 0.2).

Figure 3

A) Brain regions showing differential activity as a function of age. Activations, threshold p < 0.05, svc are rendered on a representative high resolution anatomical scan. B) Plot of activity in the ventral striatum (circled in A) response to …

The main effect of response map (nogo versus go) identified regions differentially engaged as a function of cognitive control demands including the right inferior frontal gyrus (IFG; x = 32, y = 23, z = 3), showing significantly greater responses to nogo relative to go trials (p’s < 0.05, whole brain corrected; Figure 4A). Post-hoc analyses testing for the best fitting function indicated the right IFG response was significantly explained by a linear function (F(1,59) = 4.53, p = 0.037) and not a quadratic function (F(1,59) = .17, p > 0.6). Posthoc analyses indicated that the right IFG also showed greater activity to calm relative to happy faces (F(2,59) = 8.95, p < 0.005). Further, the right IFG ROI showed a linear decrease in response magnitude with increasing age to nogo trials relative to go trials (r(61) = −0.28, p = 0.026; Figure 4B).

Figure 4

A) Brain regions showing differential activity as a function of task (nogo > go). Activations, thresholded p < 0.05, whole brain corrected are rendered on a representative high resolution anatomical scan. B) Plot of activity in the right …

When controlling for performance effects, the task x age interaction in the right IFG was no longer significant (p > 0.4), indicating performance was a more robust predictor of activity in the right IFG than age. This relationship was demonstrated by a significant correlation between response magnitude to correct nogo vs. go trials and overall performance (as measured by false alarm rate; r(61) = 0.39, p = 0.002; see Figure 4C), which was replicated in the control analysis with a matched number of trials (r(61) = 0.28, p = 0.026). Figure 4C depicts this relationship with one participant excluded who was found to be an extreme outlier (defined as more than three interquartile ranges above the third or below the first quartile value). Although the correlation is significant including this individual, excluding this individual renders the resulting correlation even more reliable (r(60) = 0.45, p < 0.001). All reported analyses represent responses to correct trials. Thus, individuals more susceptible to false alarms tend to recruit the right IFG more to the nogo trials for which they successfully suppressed a behavioral response.

Limitations

The findings presented here should be considered in light of their limitations. First, it should be explicitly acknowledged that a third emotional category, fearful faces, was present during the experimental task and the focus of a previous report (Hare et al., 2008). The calm face condition served as a control condition in both reports. Though behavioral findings suggest the presence of fearful faces in a functional scan did not modulate behavioral accuracy differently than the other two emotion categories, it is possible that the presence of fearful faces influenced the findings in ways to which the available measures were not sensitive. In addition, happy faces differ from calm faces in valence and salience, both of which could have contributed to the observed effects of appetitive value. A second methodological limitation is in the use of calm faces as a control condition. Though normative data suggest that calm faces are less positive and arousing than happy faces (Tottenham et al., 2009), we did not explicitly collect these ratings and it is possible that the calm faces were interpreted as mildly positive in their own right. In terms of results, the modest nature of the coactivation findings should also be acknowledged. Finally, measures of pubertal status and endogenous hormones were not acquired. Seminal research has demonstrated ways in which circulating gonadal hormones affect both organizational and activational mechanisms to influence brain function across development (Romeo & Sisk, 2001; Sisk & Foster, 2004; Steinberg, 2008) and shown a predictive relationship between pubertal status and such appetitive behaviors as sensation seeking and drug abuse (Martin et al., 2002; see Forbes & Dahl, 2010). Future research including measures of hormones may inform the relationship between striatocortical development, hormonal maturation, and behavioral outcomes (Blakemore, Burnett, & Dahl, 2010).

We gratefully acknowledge the assistance of Doug Ballon, Adriana Galvan, Gary Glover, Victoria Libby, Erika Ruberry, Theresa Teslovich, Nim Tottenham, Henning Voss, and the resources and staff at the Biomedical Imaging Core Facility of the Citigroup Biomedical Imaging Center at Weill Cornell Medical College. This work was supported by National Institute of Mental Health grants P50MH062196 and P50MH079513, National Institute of Drug Abuse grants R01DA018879 and T32DA007274, and National Institute of Mental Health Fellowship F31MH073265, as well as K99 MH087813 (LHS).