Manually segmented hippocampal ROIs were used to conduct small vo

Manually segmented hippocampal ROIs were used to conduct small volume correction in statistical analyses. Statistical analysis was performed in a factorial framework implemented in SPM5, with treatment group as the between subject factor and time as within subject factor. Small-volume correction was applied using the hippocampal ROI generated above. Family-wise error (FEW) multiple-comparison selleck chemical correction was applied to all statistical tests with a corrected height threshold of p = 0.05. Surface renderings of whole-brain and hippocampus ROIs

were generated in 3D Slicer (www.slicer.org) using a volume ray casting rendering equation. Maximum intensity projections of T- and F- statistic find protocol maps were rendered and overlaid onto the ROI isosurfaces using the volume render module in 3D Slicer. The glutamate biosensor (model 7004, Pinnacle Technologies) was established to be sensitive to micromolar concentrations of glutamate, able to detect rapid changes in extracellular glutamate efflux (<0.5 s response), selective for glutamate, and sufficiently small

(130 μM dimension sensing probe) to selectively measure changes within hippocampal subregions (see Hu et al., 1994). On the morning of the experiment, glutamate biosensors were calibrated against glutamic acid using a three-step concentration curve applied over 3 min. The selectivity of the biosensor response was then tested by administration of 125 mM aminophylline of ascorbic acid and tracing observed over an additional 3 min. After precalibration, the biosensors were rinsed in ultra-pure H2O and then immediately implanted via the arm of the stereotax for in vivo recording. Sensitivity of the biosensor response (at least 3 nA per 10 μM glutamic acid), stability of the response, as well as a nonresponse to ascorbic acid were required for the biosensor to be inserted for in vivo experiments. Mice were anesthetized with chloral hydrate (400 mg/kg) and placed in a stereotax with heating pad postinduction,

at which time a 0.6 mm i.p. catheter for drug delivery was inserted. Calibrated biosensors were then directly inserted into the ventral hippocampus and connected to the potentiostat for in vivo recording. Following baseline stabilization of extracellular glutamate signal over one hour, ketamine 30mg/kg or saline was administered via the i.p. catheter and extracellular glutamate response recorded from each hippocampal subregion location over the following 30 min (Bregma coordinates EC A-P 4.7, M-L 3.0, D-V 4.0; dentate gyrus A-P 3.4, M-L 2.7, D-V 3.8; CA1 subfield A-P 3.5, M-L 3.25, D-V 4.2; subiculum A-P 3.9, M-L 3.25, D-V 4.2.) Postexperiment, animals were removed from the stereotax, overdosed with chloral hydrate and euthanized by cervical dislocation.

The

The Dolutegravir solubility dmso timing of CO2-evoked Ca2+ responses in both AFD and BAG correlated with peaks in locomotory activity (Figure 6A). We investigated these correlations directly by ablating AFD and/or BAG and examining behavioral responses (Figure 6B). For statistical comparison, we chose time intervals before and after gas switches according to the occurrence of peaks in wild-type behavioral rates. In the absence of food, neither AFD nor BAG ablation abolished modulation of speed across shifts in CO2 (Figures 6B and S4). Stronger phenotypes were observed for reversal and omega rates (Figure 6B). Unexpectedly, ablation of AFD increased reversal and omega rates following

a sharp CO2 rise (ttx-1, Figures 6B, 7B, 7C, 7H, and 7I) and reduced suppression of omega turns following a CO2 fall (ttx-1, Figures 6B, 7K, and 7L), suggesting that AFD acts to suppress reversals and omega turns at these two time points. Ablation of BAG abolished reversal and omega responses to a rise in CO2 (pBAG::egl-1, Figures 6B, 7B, 7C, 7H, and 7I) and reduced the suppression of omega turns following a CO2 fall (pBAG::egl-1, Figures 6B, 7K, and 7L), consistent with BAG excitation promoting reversals and omega turns. Coablation of AFD and BAG abolished the suppression of reversals and omega turns following a

fall in CO2 (ttx-1; pBAG::egl-1, Figures 7F and 7L). This effect was due to reduced reversal and omega rates under prolonged high CO2 (ttx-1; pBAG::egl-1, Rucaparib clinical trial red bars, Figures 7E and 7K). These data suggest that together BAG and AFD act to suppress reversals and omega turns when CO2 decreases. Curiously, AFD-ablated BAG-ablated animals continued to show a transient increase in reversals following a CO2 rise (ttx-1; pBAG::egl-1, Figures 6B, 7B, and 7C). This result suggests that there is at least one other CO2 “ON” sensory neuron, XYZ, that promotes reversals in response to a CO2 rise. It also suggests that after a CO2 rise, AFD acts antagonistically to both BAG and the hypothetical XYZ neuron to inhibit reversals. We

investigated whether the ASE or AQR, PQR, URX neurons could be XYZ by ablating them together with AFD and BAG. Ablating ASEL/R had no significant effect on unless the reversal rate of AFD-ablated BAG-ablated animals immediately following a CO2 rise (che-1; ttx-1; pBAG::egl-1, Figures S5A–S5D) but did alter reversal rates under prolonged high CO2 ( Figures S5E and S5F). The ablation of AQR, PQR, URX by an integrated pgcy-36::egl-1 transgene caused an increase in the reversal rate of AFD-ablated BAG-ablated animals in air alone ( Figures S5A–S5D). These data suggest that the ASE neurons suppress reversals under prolonged high CO2 and that the AQR, PQR, URX neurons suppress reversals in the absence of CO2. However, even animals defective in AFD, BAG, ASE, AQR, PQR, and URX retained some CO2 responsiveness, suggesting that C. elegans has additional CO2 sensors. Wild-type C.

, 2013) Removal of spatially structured noise has been greatly i

, 2013). Removal of spatially structured noise has been greatly improved by an automated “FIX” denoising algorithm (Smith et al.,

2013b). The fMRI data of interest are restricted to gray matter (white matter and nonbrain voxels are largely irrelevant to this analysis). At the 2 mm spatial resolution appropriate for the fMRI data, there are ∼90,000 “grayordinates” (surface vertices for cortex and voxels for subcortical domains). TSA HDAC order Analysis of functional connectivity entails computing the correlation of time series data for 90,000 × 90,000 grayordinates. This amounts to ∼33 GB of data for a “dense connectome” when stored in the recently introduced “CIFTI” grayordinate × grayordinate file format; the data files would be ×6-fold larger if stored learn more in a conventional voxel-based volumetric format (Glasser et al., 2013a). More generally, the CIFTI format provides efficient and flexible way of representing many types of data used by the HCP, including task-fMRI and dMRI results. One widely used way to analyze fcMRI data involves seed-based correlations,

which reveals the spatial pattern associated with any given region of interest (ROI), be it a single seed point or a larger collection of grayordinates or conventional voxels. For example, Figure 5 compares the fcMRI seed-based correlations (column 2) in individual (top row) and a group average (generated from 120 subjects). The selected seed in parietal cortex (black dot, green arrows) reveals a pattern of strong correlations and anticorrelations in several distant regions of frontal, occipital, and temporal cortex (arrows). The high quality of HCP data acquisition and analysis provides notably fine spatial detail for a single grayordinate seed in each individual subject with minimal smoothing of the data.

The group average pattern is similar to the individual but is much blurrier, because the alignment is imperfect but also presumably because there is noise in each of the individual subject maps, as well as biological variation between individuals. One way to examine the specificity is by crossmodal comparisons, using cortical ever myelin maps (column 3) and task fMRI (column 4), that are part of standard HCP data acquisition and processing. The fcMRI patches correspond with patches of heavy cortical myelin (Figure 5C, black dots, arrows). There is also a correlation with the task fMRI results in Figure 5D, which shows the activation pattern from viewing faces in the HCP “Emotion” state. The intersubject registration used in Figure 5 was based only on shape features, using FreeSurfer’s “sulc” maps and registration algorithm (column 1). Alignment can be further improved using a novel multimodal surface matching (MSM) algorithm (Robinson et al., 2013; E.C. Robinson, S. Jbabdi, M.F. Glasser, J. Andersson, G.C. Burgess, M.P. Harms, S.M. Smith, D.C.V.E., and M.

While this is probably an oversimplification given the complex dy

While this is probably an oversimplification given the complex dynamics of the brain, there is no reason to think that there would be complex differences in dependencies across SWRs before correct and incorrect trials that would result in illusory significance values for our analyses. We also chose to use the number of cell pairs, individual cells, or trials as the N in our statistical analyses, as is standard in the field. We note here that our results are highly significant and consistent

across individual animals and across tracks. We also carried out a complementary analysis to determine whether we could predict the outcomes on individual trials. Our goal here was to use a measure that allowed us to combine multiple run sessions from multiple

GSK2656157 cost animals together, and as each run session was associated with a different number of recorded place cells, we measured the proportion of possible cell pairs that were active before each trial. We calculated, for each run session, the total number of possible coactive cell pairs, which is (number place cells recorded) × (number place cells recorded − 1)/2. We then determined, for each trial, the number of those cell pairs that were coactive within an SWR preceding that trial and then divided that number by the total to get a proportion. Given that measure for each correct selleck compound or incorrect trial, we then used logistic regression to relate the proportion of coactive cells to the trial outcome (correct or incorrect). The model was estimated

based on half of the total data, subsampled to include an equal number of correct and incorrect trials from each run session. The specific correct and incorrect trials were chosen at random. We then tested the model prediction on the other half of the data, once again subsampled to include an equal number of correct and incorrect trials from each run session. We repeated that estimation and testing process 1,000 times with different sets of correct and incorrect trials to produce a distribution of predictions and PDK4 compared that distribution across performance categories and to chance performance of 50% correct. We also examined the content of individual SWRs. We used our previously developed decoding approach (Karlsson and Frank, 2009) to translate the activity of neurons active during the SWR to a trajectory through space. Briefly, for all SWRs with at least two active place cells, we divided the SWR into 15 ms bins and for each bin used the place fields of neurons active in that bin to derive a probability distribution function over distance from the end of the center arm. For each bin, that pdf represented where we would expect the animal to be on the track given that those cells had fired the observed numbers of spikes. To determine whether a given decoded trajectory was best described as inbound or outbound, we fit a line to samples from the sequence of pdfs plotted versus time.

As predicted (Long et al , 2009), JZL184 decreased

As predicted (Long et al., 2009), JZL184 decreased VE-822 concentration core temperature across time (Figure 7D; F(3,34) = 2.63, p < 0.01). To definitively test whether JZL184 increases 2AG levels during reward seeking, we assessed lipid content in VTA tissue from JZL184 and vehicle-treated

rats upon completion of the ICSS-VTO task and found that JZL184 significantly increased 2AG VTA tissue content in comparison to vehicle (Figure 7E; t(27) = 2.07, p = 0.048), thereby confirming that JZL184 augments 2AG levels in the VTA during reward directed behavior in the rat. To assess the effects of increasing 2AG levels on the neural mechanisms of reward seeking we treated rats with JZL184 (10 mg/kg i.v.) while responding was maintained by brain stimulation reward in the ICSS-VTO task. As observed using a cumulative dosing approach, JZL184 Selleckchem Torin 1 (10mg/kg i.v.) decreased response latency (Figure 8A; t(14) = 2.36, p = 0.033; mean values: b = 3.55, v = 3.48, JZL = 2.89 s). Enhanced reward seeking occurred in parallel with an increase in cue-evoked

dopamine concentration ( Figure 8B; F(2,14) = 10.86 p < 0.01; 10 mg/kg versus vehicle, p < 0.01; also see Figure S3B for mean dopamine concentration traces). The effect of JZL184 on dopamine signaling during individual trials is illustrated by the representative color plots and accompanying dopamine concentration traces ( Figure 8C), while the effect of JZL184 on dopamine signaling across trials is shown by the representative surface plot ( Figure 8D). To confirm that 2AG levels within the VTA are alone sufficient to facilitate the neural mechanisms of reward seeking, we infused JZL184 into the VTA while measuring dopamine concentrations and behavior maintained in the ICSS-VTO task. Although the required vehicle to achieve solubility (a 6 μg/0.5 μl solution required 100% dimethyl sulfoxide [DMSO]) increased response latency; remarkably, intrategmental JZL184 (6 μg, ipsilateral) reversed the DMSO-induced deficits in

reward seeking ( Figure 8E; t(6) = −2.51, p = 0.046; mean values: b = 3.75, DMSO = 4.61, JZL = 3.47 s) while increasing cue-evoked dopamine concentrations ( Figure 8F; F(2,18) = crotamiton 10.84 p < 0.01; 6 μg versus vehicle, p = 0.023). To verify that the effects of intrategmental JZL184 on reward seeking were CB1 receptor dependent, we then treated rats with a subthreshold dose of rimonabant (1.25 mg/kg i.v.), which reverted response latencies to DMSO conditions. The effects of intrategmental DMSO and JZL184 on cue evoked dopamine events occurring in individual trials are illustrated by the representative traces in Figure 8G, whereas the effects across trials are depicted in a representative surface plot ( Figure S4C). JZL184-induced increases in cue-evoked dopamine concentration and reward seeking can also be observed by viewing audio-visual material ( Movie S3).

The AS event generating

The AS event generating selleck screening library the Gls-l and Gls-s isoforms was listed as a top target in our Aspire2 AS analysis, with a validated ΔI of −0.3, (Figure 6B and Table S7). Quantitative RT-PCR using primers specific for each Gls isoform demonstrated that in Elavl3−/−;Elavl4−/− DKO brain, abundance of the Gls-s isoform did not change while abundance of the Gls1-l isoform was reduced to approximately 50% of the WT levels ( Figure 6D). Western blot analysis using an antibody recognizing a common epitope to both isoforms also demonstrated that the abundance of Gls-s and Gls-l proteins were reduced to 60% and 25% of the WT levels, respectively ( Figures

6C and 6E). Since Elavl3/4 DKO die at age P0 it is difficult to further carry out any physiological analyses. We assessed whether Elavl3−/− single KO mice also exhibited a defect in glutamate regulation and observed a smaller but significant decrease in

total glutamate levels and in Gls-l, but not Gls-s, protein levels ( Figure S5). These results point to a role for nElavl proteins in directly controlling Gls-s and Panobinostat purchase Gls-l levels in the nervous system through reinforcing mechanisms of involving both the regulation of AS and mRNA half-life, consistent with nElavl HITS-CLIP results demonstrating direct binding to both intronic and 3′UTR elements. To assess whether there might be a physiologic correlate of excitation/inhibition imbalance manifested by misregulation of glutamate signaling in Elavl3−/− mice, we undertook an EEG analysis of cortical function. Video EEG monitoring of awake and behaving mutants revealed a striking pattern of abnormal cortical hypersynchronization in both Elavl3+/− and Elavl3−/− mice never seen in WT mice ( Figure 7A; through Movie S1). In Elavl3+/− mice, there was a nearly continuous presence (1–9/min) of bilaterally synchronous sharp cortical

spike discharges, sometimes accompanied by brief afterdischarges ( Figure 7B). Elavl3−/− mice displayed similar discharges as well as more severe, non-convulsive electrographic seizures lasting from 10–30 s ( Figure 7C). Both patterns demonstrate aberrant hypersynchronization in cortical networks. Until recently studies aimed at identifying regulatory RNA sequences have been limited to correlative information lacking direct functional links to biological processes. HITS-CLIP technique provides a methodology to identify such functional RNA-protein interaction sites and has been successfully applied to identifying binding sites and uncovering new biological functions for several RNABPs, including Nova (Licatalosi et al., 2008), PTB (Xue et al., 2009), hnRNP C (König et al., 2010), TIA-1 (Wang et al., 2010b), TDP-43, and Fox2 (Yeo et al., 2009).

24 ± 1 51 Hz; n =

5) significantly reduced thalamocortica

24 ± 1.51 Hz; n =

5) significantly reduced thalamocortical neurotransmission in comparison to WT mice (amplitude: 11.84 ± 0.84 pA; frequency: 10.47 ± 2.14 Hz; n = 3). We did not detect any thalamocortical synaptic response at P4–P6 in ThVGdKO mice (n = 4) and detected only very weak response in some slices at P13–P15 that was similar in amplitude and frequency to that observed at P9–P11 and much smaller than that observed in control littermates (p < 0.001; Figures 1F and 1G). These results indicate that Vglut1 and Vglut2 can both contribute to glutamatergic neurotransmission at thalamocortical synapses, and elimination of both Vglut1 and Vglut2 in ThVGdKO mice nearly completely abolishes thalamocortical neurotransmission. We confirmed these results using in vivo electrophysiological techniques in P9–P12 mice (Figures 1H and 1I). Local field potentials (LFPs) recorded with extracellular multisite silicon array electrodes in somatosensory S3I-201 cortex in response to peripheral whisker stimulation typically produce brief multiphasic events that

are dominated by an initial negative-going waveform with greatest amplitude in L4 (Quairiaux et al., 2007). Stimulus-triggered waveform averages in control (Vglut1−/−;Vglut2fl/−) mice showed robust evoked LFPs ( Figure 1I, left; maximum negative amplitudes of 207 μV and 209 μV; maxima at 38 ms and 51 ms, check details respectively, after stimulus onset; waveform widths at half maximum were 23 ms and 31 ms). The same experimental procedure in ThVGdKO mice failed to elicit evoked potentials (n = 4). Indeed, the stimulus-triggered

waveform averages revealed no stimulus-related activity in the LFPs at all ( Figure 1I, right). Histology confirmed that the recording probes were placed in similar locations within somatosensory cortex in both groups of mice (data not shown). Together, these results indicate that glutamatergic neurotransmission at thalamocortical synapses in ThVGdKO somatosensory cortex was largely, if not completely, abolished. over Barrels in the somatosensory cortex of mice are composed of clusters of thalamocortical axon arbors in L4 surrounded by rings of spiny stellate neuron somata whose dendrites are oriented toward the center of the barrel to synapse with thalamocortical afferents relaying information from a single whisker (Li and Crair, 2011). We used cytochrome oxidase (CO) histochemistry and Nissl staining to examine whether cortical barrel formation was dependent on thalamocortical glutamatergic neurotransmission. In flattened tangential sections through somatosensory cortex, clear CO barrel patterns were present in Vglut1−/−,Vglut2fl/− and all other control mice, while a barrel pattern was not detectable in ThVGdKO mice ( Figure 2A, top; Figure S1A available online). This suggests that thalamocortical afferents fail to cluster into barrels in ThVGdKO mice.

Spine classification analysis revealed

a higher fraction

Spine classification analysis revealed

a higher fraction of filopodia-like and thin spines compared to stubby and mushroom spines in Fmr1 KO neurons. In contrast, the fraction of filopodia-like spines was significantly decreased with a concomitant elevation of mature stubby/mushroom spines in dKO neurons, comparable to levels in WT and S6K1 KO neurons ( Figure 5C). No appreciable differences were found in spines of other types ( Figure S5). These findings demonstrate that removing S6K1 can counteract dendritic morphology abnormalities associated with loss of FMRP. To assess whether the genetic reduction of S6K1 in Fmr1 KO mice could alleviate autistic-like behaviors, we performed an array of tests on all four genotypes. It has been reported that the behavior of Fmr1 KO mice in these tests Roxadustat ic50 is sensitive to genetic background, age, and GSK2118436 concentration the experimental paradigms used ( Dobkin et al., 2000; Spencer et al., 2011). Therefore, we designed our behavioral battery to include tests that have reported consistent differences between WT and Fmr1 KO mice and have been conducted previously in our laboratory ( Hoeffer et al., 2008). To examine motor coordination and skill acquisition, we tested performance on the rotarod using parameters similar to those described previously (Spencer et al., 2011). Fmr1 KO mice were significantly impaired in learning and motor coordination across eight trials over two days as compared to their WT littermates

( Figure 6A). In contrast, the dKO mice displayed markedly higher motor coordination and acquisition, performing consistently

better than the other three genotypes on both days of the test ( Figure 6A). Motor coordination and acquisition of S6K1 KO mice was diminished, which differs from previous findings ( Antion et al., 2008b), perhaps due to background differences. Taken together, the rotarod results indicate that removal of S6K1 corrects impaired motor coordination and motor skill acquisition in FXS model mice. Deficits in appropriate L-NAME HCl social interaction and perspective taking are one of the core features of autism spectrum disorders (ASDs). Therefore, we employed the three-chambered social interaction test to determine whether removal of S6K1 could prevent impaired/inappropriate social behavior shown by Fmr1 KO mice. In the social approach test, experimental mice interacted more with the stimulus mouse compared to the object across all genotypes ( Figure S6D). When the object was replaced by a novel mouse in the next phase of the test, clear genotype-specific differences emerged ( Figure 6C). Fmr1 KO mice failed to distinguish between the familiar and novel mice, whereas WT and dKO mice spent significantly more time interacting with the novel mouse. S6K1 KO mice showed a trend toward increased interaction with novel stimuli. These findings indicate that removing S6K1 can correct deficits in social interactions displayed by FXS model mice.

g , a click) has frequency components that align at their peaks (

g., a click) has frequency components that align at their peaks (phase 0). For sounds dominated by one of these feature types, adjacent modulation

bands thus have consistent relative phase in places where their amplitudes are high. We captured this relationship with a complex-valued correlation measure (Portilla and Simoncelli, 2000). We first define analytic extensions of the modulation bands: αk,n(t)≡b˜k,n(t)+iH(b˜k,n(t)), where H   denotes the Hilbert transform and i=−1. The analytic signal comprises the responses of the filter and its quadrature twin, and selleck compound is thus readily instantiated biologically. The correlation has the standard form, except it is computed between analytic modulation bands tuned to modulation frequencies an octave apart, with the frequency of the lower band doubled. Frequency doubling is achieved by squaring the complex-valued analytic signal: dk,n(t)=ak,n2(t)‖ak,n(t)‖,yielding C2k,mn=∑tw(t)dk,m∗(t)ak,n(t)σk,mσk,n,k ∈ [1…32], m ∈ [1…6], and (n −

PF-06463922 m) = 1, where ∗ and ‖⋅‖ denote the complex conjugate and modulus, respectively. Because the bands result from octave-spaced filters, the frequency doubling of the lower-frequency band causes them to oscillate at the same rate, producing a fixed phase difference between adjacent bands in regions of large amplitude. We use a factor of 2 rather than something smaller because the operation of exponentiating a complex number is uniquely defined only for integer powers. See Figure S6 for further explanation.

C2k,mn is complex valued, and the real and imaginary parts must be independently measured and imposed. Example sounds with onsets, offsets, and impulses are shown in Figure 3D along with their C2 correlations. In total, there are 128 cochlear marginal statistics, 189 cochlear cross-correlations, 640 modulation band variances, 366 C1 correlations, and 192 C2 correlations, for a total of 1515 statistics. Synthesis was driven by a set of statistics measured for a sound signal of interest using the auditory model described above. The synthetic signal was initialized with a sample of Gaussian white noise, and was modified with an iterative process until it shared the measured also statistics. Each cycle of the iterative process, as illustrated in Figure 4A, consisted of the following steps: (1) The synthetic sound signal is decomposed into cochlear subbands. We performed conjugate gradient descent using Carl Rasmussen’s “minimize” MATLAB function (available online). The objective function was the total squared error between the synthetic signal’s statistics and those of the original signal. The subband envelopes were modified one-by-one, beginning with the subband with largest power, and working outwards from that. Correlations between pairs of subband envelopes were imposed when the second subband envelope contributing to the correlation was being adjusted.

Further analyses support the hypothesis that age-related changes

Further analyses support the hypothesis that age-related changes are based on the development of behavioral control abilities rather than social norm understanding and social abilities. Indeed, when performing a median-split on age in Study 1 to analyze the responder behavior, we observed that younger children were more willing to accept unfair offers of one MU than older children (χ21 = 9.0, p = 0.01; Figure 1C). Astonishingly, these age-related differences in rejection behavior occurred despite comparable fairness judgments across age; that is, children of different ages showing already an equal understanding of which offer was fair and which not (see Figure S1C). Responders

were also asked to rate how they had felt when seeing the offer on three

scales asking for happiness, sadness, and anger ranging from “very” to “not at all.” Again, there were no differences http://www.selleckchem.com/products/MLN-2238.html in rated emotions on any of the three scales between the two age groups, neither when accepting offers (happiness: F[1,52] = 1.05; p = 0.309; sadness: HSP inhibitor drugs F[1,52] = 3.23; p = 0.078; anger: F[1,52] = 0.09; p = 0.766; Figure S1D) and more importantly nor when rejecting offers (happiness: F[1,10] = 2.03; p = 0.185; sadness: F[1,10] = 0.47; p = 0.509; anger: F[1,10] = 0.00; p = 0.987; Figure S1E). Another indicator for age-related differences in behavioral control were findings from Study 2, where the degree of strategic behavior was correlated with behavioral control as measured by SSRT scores (r = −0.578, p = 0.001; Figure 1F) as well as age (r = −0.558, p = 0.002; ρ = −0.563; p = 0.002). Importantly, strategic behavior in both studies was unrelated to performance on measures of perspective taking, empathic concern, risk taking, or general intelligence (see Experimental Procedures for details on the measures and Tables S1) and no age differences could be found on fairness judgments (Figures S1B and S2B), proposers’ beliefs about the responders’ decision

(Figure S2C), or what proposers indicated they would else have done in the role of responder (Figure S2D). Thus, in two independent studies, we show that the degree of strategic behavior increases with age and demonstrate that this is linked to age-related differences in the ability to implement behavioral control and not to developmental differences in social preferences, knowledge about social norms or beliefs about the others, social skills such as cognitive or affective perspective taking, risk preferences, or general cognitive abilities. Analysis of the proposer behavior in adults revealed that offers were larger in the UG than in the DG (t13 = 7.75, p < 0.001, Figure S2E), showing that adults also demonstrate strategic behavior. In the analyses of the imaging data of Study 2, we opted for a region of interest (ROI) approach (Kriegeskorte et al., 2009).