The time course of signals related to the sum and difference in the temporally discounted values for the left and right targets emerged immediately and nearly simultaneously in the CD and VS. This was true regardless of whether the results from these two areas were compared using the fraction of neurons showing significant effects of each variable (Figure 6A) or the proportion of the variance in neural activity attributed to a given variable (coefficient of partial determination,

find more CPD; Figure 6B). Average CPD for the difference in the temporally discounted values reached their maximum values 200 and 175 ms from the cue onset for the CD and VS, whereas the values for the sum reached their maximum 225 ms and 250 ms from the cue onset for the CD and VS, respectively (Figure 6B). In contrast, signals related to the difference in temporally discounted values for the chosen and unchosen Selleck KU57788 targets and the animal’s choice arose more slowly and gradually during the cue period (Figure 6). In both CD and VS, the latencies of the signals related to the sum and difference in the temporally discounted value for the left and right targets were both shorter than those related to the animal’s choice (Kolmogorov-Smirnov tests, p < 0.05; Figures S1A and S1B). The latencies

of the signals related to the difference in the discounted values for the chosen and unchosen targets and the animal’s choice Aconitate Delta-isomerase were not statistically different in either CD (p > 0.3) or VS (p > 0.2), and none of the signals related to the values or choice showed significant differences in their latencies between the CD and VS (p > 0.1). It has been shown that the signals related to the value of chosen option arise in the primate orbitofrontal

cortex immediately after the stimulus onset (Padoa-Schioppa and Assad, 2006), whereas other studies found that similar signals might develop more gradually in the striatum (Lau and Glimcher, 2008 and Kim et al., 2009b) as well as in the rodent frontal cortex (Sul et al., 2010). We found that the time course of these so-called chosen value signals might change depending on whether the sum of the temporally discounted values for the two targets was included in the regression model or not. In particular, when the sum of the temporally discounted values was omitted from the model, activity changes related to the temporally discounted values of the chosen target appeared much earlier (see Figures S1C and S1D). Therefore, it is important to distinguish the neural activity related to the value of the chosen target from those related to the sum of the values for alternative targets.

, 2004, Kohyama et al., 2010 and Lim et al., 2000). We then found that exogenous BMP2 inhibited proliferation, repressed neuronal differentiation, and promoted astrocyte fate to similar extents in both WT and KO SVZ-NPCs ( Figures S7I–S7K). Therefore, BMP2 had similar effects on both DG-NPCs ( Figures 6B–6G) and SVZ-NPCs. We therefore predicted that FXR2 Fulvestrant manufacturer must not regulate Noggin expression in SVZ-NPCs as it does in DG-NPCs. To assess this possibility,

we first confirmed that FXR2 indeed does not bind Noggin mRNA in SVZ-NPCs ( Figure S7L). Because FXR2 and Noggin are expressed in both the DG and SVZ, we reasoned that a lack of FXR2 regulation of Noggin in the SVZ might be due to cell type-restricted expression of these two proteins. To precisely identify the cells expressing Noggin, selleck chemicals llc we used both Noggin antibody staining and a transgenic “knock-in” mouse strain expressing β-gal under the Noggin promoter (NogginlacZ) ( McMahon et al., 1998). Expression of β-gal in this strain is an accurate and

precise reporter of Noggin expression ( Stottmann et al., 2001). Indeed, we found that FXR2 and Noggin are not colocalized in the same cells in the SVZ ( Figure 8A; Figures S8A–S8C). Noggin expression is restricted to s100β+ ependymal cells that also express Nestin ( Figures 8B and 8C, Figure S8B), consistent with a previous report ( Lim et al., 2000). By contrast, FXR2 is expressed only in s100β-negative CGK 733 NPCs (Figures 1, 8A, and 8C; Figure S8C),

and not in s100β+Nestin+ ependymal cells ( Figures 8A and 8B; Figure S8A). In the DG, however, we found that Noggin is expressed in Nestin+GFAP+ radial glia-like NPCs (Figure 8E; Figures S8E and S8G), consistent with an earlier study (Bonaguidi et al., 2008). Importantly, these cells also express FXR2 (Figure 1), and FXR2 expression colocalizes with Noggin in both NPCs and neurons of the DG (Figure 8D; Figures S8D and S8F). These spatiotemporal expression data further support the regulatory role of FXR2 in DG-NPCs, but not in SVZ-NPCs. Taken together, our data argue for a model in which FXR2 specifically regulates DG-NPCs by directly repressing Noggin expression in DG-NPCs. Because Noggin expression in the SVZ is not regulated by FXR2, FXR2 deficiency therefore has minimal impact on SVZ-NPCs (Figures 8F and 8G). The molecular mechanism behind the differential regulation of SVZ and DG neurogenesis has gone largely unexplored. By unveiling a regulatory mechanism involving FXR2 that governs adult hippocampal neurogenesis, our data show that a brain-enriched RNA-binding protein could play important roles in the differential regulation of NPCs residing in different brain regions.

While many studies have focused on turning responses evoked by stimuli that rotate about the animal, other global motion patterns can also affect fly behavior, such as motion stimuli that would be associated with forward movement, pitch, or sideslip ( Blondeau and Heisenberg, 1982, Crizotinib Duistermars et al., 2012, Götz, 1968, Götz and Wenking, 1973, Reiser and Dickinson, 2010 and Tammero et al., 2004). In walking flies, motion signals can modulate both turning and forward movements ( Götz and Wenking, 1973, Hecht and Wald, 1934 and Kalmus, 1949). Neuronal silencing experiments in freely walking flies suggested that some behavioral specialization for

translational and rotational responses exists early in visual processing ( Katsov and Clandinin, 2008). However, as freely walking flies experience complex visual stimuli, it remains unclear how neural circuits might be specialized to respond to either translational or rotational signals. In spite of this extensive analysis of motion vision in flies, central questions remain. What MDV3100 are the functional contributions of each of the input pathways from the lamina into the medulla? What are the neural mechanisms that underlie the differential tuning of motion-detecting circuits for light and dark edges? How are inputs to motion detecting circuits specialized with respect to behavior? Using quantitative behavioral assays, in vivo calcium

imaging and combinatorial genetic inactivation of the main input pathways to motion detection, we shed new light on these questions. We demonstrate that feature extraction and behavioral specialization use overlapping but distinct input channels

in the peripheral visual system. While the lamina neurons L1 and L2 have been studied in detail, we sought to identify genetic tools to analyze the function of the two remaining critical relays in the lamina, L3 and L4. To do this, we performed a forward genetic screen using conditional neuronal inactivation. We established a collection of more than Phloretin 1000 isogenic InSITE Gal4 lines ( Gohl et al., 2011). Gal4-mediated expression of a temperature sensitive dynamin allele ( Kitamoto, 2001), UAS-shibirets (UAS-shits) was used to inducibly inactivate defined subsets of neurons immediately before testing. A phototaxis assay (S. Bhalerao and G. Dietzl, personal communication) was first used to exclude lines that displayed gross defects in movement ( Figure 1C). Next, we used a population assay to quantify behavioral responses to motion ( Katsov and Clandinin, 2008). Flies walking in glass tubes on a CRT monitor were shown brief presentations of two different random dot motion stimuli in which the dots were either lighter or darker than a gray background (“increment” and “decrement”; Figure 1C). Using this paradigm, we screened 911 InSITE lines, and identified lines with behavioral deficits by comparing motion-evoked modulations of translational and rotational movements ( Figures 1D–1I).

What components might contribute to the preferential

organization of recycling vesicles near the active zone? A potential candidate is actin, the highly dynamic cytoskeletal element that is concentrated at synapses (Bloom et al., 2003; Colicos et al., 2001; Sankaranarayanan et al., 2003; Siksou et al., 2011). We tested its possible involvement by incubating slices in the actin-stabilizing selleck screening library agent jasplakinolide before and during synaptic labeling. As with synapses under basal conditions, the average fraction of recycling vesicles in jasplakinolide-treated synapses was small (0.18 ± 0.01, n = 63, Figures 6A and 6B) and similarly distributed (p = 0.32, two-tailed Mann-Whitney test, Figure 6B). Thus, actin does not have a significant role in determining the proportion of recycling vesicles available for turnover. Next, we examined its potential impact on vesicle spatial organization by generating selleck chemical cumulative frequency distance plots. Strikingly, the preferential distribution of recycling vesicles toward the active zone was abolished; both the recycling and nonrecycling pools showed a similar distribution profile (p = 0.38, two-tailed paired t test, n = 17), comparable with the nonrecycling

pool profile observed in basal conditions (Figure 6C). We examined how recycling vesicles were mixed with respect to nonrecycling vesicles by performing a cluster

analysis. This revealed a flat profile (Figure 6D) with a clear absence of the sharp peak seen under basal conditions and was consistent with a homogeneous mixing of the two pools within the synapse. Taken together, our findings suggest that the impairment of actin remodeling during exo-endocytic vesicle turnover disrupts the overall spatial segregation of recycling vesicles. The selective effect of jasplakinolide treatment in disrupting spatial segregation allowed us to test for a possible impact of vesicle organization on release properties. Slices were incubated in jasplakinolide or vehicle and subsequently Thiamet G FM dye labeled and destained (Figure 6E) so that we could explore the effects of disrupting the positioning of vesicles on exocytotic kinetics. Fluorescent puncta underwent effective activity-evoked dye loss in both conditions (Figure 6F) but the destaining timecourse was significantly slower in jasplakinolide-treated synapses (p = 0.003, two-tailed Mann-Whitney test, Figure 6G). Although we cannot definitively rule out other possible direct effects of actin disruption on vesicle turnover, our findings provide evidence that the preferential spatial segregation of recycling vesicles serves to increase the efficacy of fast sustained neurotransmitter release.

We show that surface delivery of GLR-1 and SOL-1 occurs in the absence of SOL-2; however, the stability or function of the complex appears compromised in sol-2 mutants. In sol-1 mutants, the

remaining components of the GLR-1 complex are also delivered to the postsynaptic membrane, indicating find more that SOL-1 does not have an essential role in assembly or trafficking of the signaling complex. We demonstrate that GLR-1-mediated currents depend on both SOL-1 and SOL-2 and that currents in sol-1 and sol-2 mutants can be rescued in adults, thus demonstrating an ongoing role for these CUB-domain proteins in synaptic transmission. Remarkably, we found that the extracellular domain of SOL-1 secreted in trans is sufficient to rescue glutamate-gated currents in sol-1 mutants. This rescue depends on in cis expression of SOL-2. Finally, we show that glutamate- and kainate-gated Duvelisib supplier currents are differentially disrupted in sol-1 and sol-2 mutants and that SOL-2 contributes to the kinetics of receptor desensitization. In summary, our results demonstrate that SOL-2 is an essential component of GLR-1 AMPAR complexes at synapses and contributes

to synaptic transmission and behaviors dependent on glutamatergic signaling. AVA interneurons in C. elegans are part of a locomotory control circuit that primarily regulates the direction of a worm’s movement. These interneurons receive glutamatergic synaptic inputs and express GLR-1, STG-2, and SOL-1—essential transmembrane proteins that contribute to a postsynaptic iGluR signaling complex ( Brockie et al., 2001a; Maricq et al., 1995; Wang et al., 2008; Zheng et al., 2004). Using in vivo patch-clamp electrophysiology, we recorded rapidly activating and desensitizing currents in wild-type

Flucloronide worms in response to pressure application of glutamate ( Figure 1A). In sol-1 mutants, glutamate-gated currents rapidly desensitize and consequently we cannot measure the currents using conventional drug application ( Figure 1A; Walker et al., 2006b). A secreted form of SOL-1 that lacks the transmembrane domain (s-SOL-1) can partially rescue the glutamate-gated current when expressed in the AVA neurons of transgenic sol-1 mutants ( Figure 1A; Zheng et al., 2006). This result suggested that s-SOL-1 formed a functional complex with GLR-1 and STG-2. To test sufficiency of s-SOL-1, we asked whether we could record glutamate-gated currents from muscle cells that coexpressed GLR-1, STG-1, and s-SOL-1. Muscle cells in C. elegans do not express any known iGluRs, STGs, or SOL-1 proteins and thus are ideal for reconstitution studies. We reliably recorded large, rapidly activating inward currents in response to pressure application of glutamate when full-length SOL-1, STG-1, and GLR-1 were coexpressed in muscle cells ( Figure 1B). In contrast, we were unable to record appreciable currents in cells that expressed s-SOL-1 instead of full-length SOL-1 ( Figure 1B).

This should affect INaP-dependent bursting ( Rybak et al., 2003). To theoretically investigate the effect of changing [Ca2+]o and [K+]o on neuronal bursting behavior, we used a single-compartment computational model of the Hb9 cell. In this model, we explicitly simulated a negative voltage shift of INaP activation with a reduction of [Ca2+]o ( Figure 3A). For VmNaP1/2 = –52 mV (at [K+]o = 6 mM), the model exhibited tonic spiking activity ( Figure 3B, top). Bursting

activity appeared at VmNaP1/2 = –53 mV ( Figure 3B, middle), and further shifting VmNaP1/2 to the left produced stable bursting with higher spiking frequency selleck compound within bursts ( Figure 3B, bottom). As expected, depolarizing the neuron by injecting current increased bursting frequency ( Figure S4, top). Bursts disappeared when the conductance of

INaP was set to 0 (to simulate the effect of riluzole, Figure S4, bottom). To investigate how random distribution of neuronal parameters could selleck kinase inhibitor affect neuron bursting properties and the relative number of pacemaker neurons involved in population bursting, we simulated a population of 50 uncoupled neurons. To provide a necessary heterogeneity in bursting properties of neurons, we randomly distributed the base values of neuronal VmNaP1/2 (i.e., these values at [Ca2+]o = 1.2 mM and [K+]o = 4 mM) among neurons using the uniform distribution within the interval [−53, −48] mV. An additional heterogeneity was set by normal distribution of all conductances, including that for INaP. The average values and variances used for all conductances can be found in the Supplemental Experimental Procedures. Because of the random distributions

used, some neurons with more negative VmNaP1/2 and/or higher values of INaP maximal conductance were intrinsic bursters, whereas the Carnitine dehydrogenase remaining neurons had no bursting capabilities. This was equivalent to our experimental data showing that bursting Hb9 interneurons had more negative values of the activation threshold and half-activation voltage for INaP than nonbursting neurons ( Table S3). After parameter distributions, simulations were run to check the ability of each neuron to generate bursts with changes in [Ca2+]o and [K+]o and to determine the percentage of bursting cells in the population at each level of [Ca2+]o (from 1.2 to 0.9 mM with 0.1 mM steps) and [K+]o (from 4.0 to 6.0 mM with 0.5 mM steps). Each 0.1 mM decrease in [Ca2+]o reduced VmNaP1/2 in all neurons by 1 mV (hence shifting INaP activation to more negative values of voltage); each 0.5 mM increase in [K+]o resulted in the corresponding reduction of EK and EL (see above). The results are summarized in Figure 3C. Specifically, none of the neurons exhibited bursting at base levels of [Ca2+]o and [K+]o (1.2 and 4 mM, respectively).

We find that, similar to the results with cultured neurons, AAK1 siRNA increased proximal branching in vivo (Figures 6I and 6J). Next, we investigated Rabin8′s function on dendrite development and spine

maturation in hippocampal cultures. Immunostaining of endogeneous Rabin8 by anti-Rabin8 antibody showed that Rabin8 is enriched in the Golgi (colocalized with Golgi marker GM-130; Figure 7A), in agreement with the role of Rab8 in post-Golgi trafficking. We first examined its function by mutating the Rabin8 phosphorylation Imatinib site and expressing these mutants in dissociated hippocampal neurons. We made the Rabin8 phospho mutant, where S240 as well as T241, S242, and S243 were mutated to Alanine (Rabin8-AAAA), which cannot be phosphorylated (Figure 5F), or to Glutamate (Rabin8-EEEE) as a putative phosphomimetic mutant. We found that these Rabin8 mutants and Rabin8 siRNA (Figures S6A and S7A) did not affect dendrite branching (Figures

S6C–S6F), indicating that Rabin8 phosphorylation by NDR1 is likely not involved in limiting dendrite branching. The total dendrite length was reduced by Rabin8-AAAA but not Rabin8 siRNA (Figure S6F). Given that Rabin8 siRNA may not have sufficiently knocked down the Rabin8 level, these observations indicate that Rabin8 is involved in dendrite growth. Next, we found that the expression of Rabin8-AAAA but not Rabin8-EEEE resulted in increased Selleckchem Galunisertib filopodia and atypical spines, and Rabin8 siRNA increased filopodia density (Figures 7B and 7C). An increase in filopodia was accompanied by a reduction in mushroom spine density by Rabin-AAAA, a trend mafosfamide that was close to reaching significance (p = 0.07). These data indicate that Rabin8 phosphorylation by NDR1/2 contributes to spine development by reducing filopodia and increasing mushroom spines. Rabin8-AAAA and Rabin8 siRNA produce less pronounced defects on spines than does NDR1/2 loss of function, possibly

because other NDR1/2 substrates act in parallel to Rabin8 and contribute to spine morphogenesis. Alternatively, it is possible that these manipulations do not completely block Rabin8 function because of their incomplete knockdown or dominant negative effect. Given that Rabin-EEEE did not alter spine or dendrite development, this mutant construct may not be able to mimic phosphorylated Rabin8, a notion reinforced by our failed attempt to rescue NDR1siRNA + NDR2siRNA’s effect on spine development with Rabin8-EEEE (Figure S6B). Since Rabin8 is involved in spine maturation, we wanted to learn if it is present in spines with synapses. With immunostaining of postsynaptic marker PSD95 and endogenous Rabin8, we observe Rabin8 in the perinuclear region resembling Golgi and inside the proximal dendrites in neurons (Figure S6G). We cannot rule out the presence of Rabin8 in spines; however, the majority of Rabin8 is found in Golgi. (Figure S6G).

Statistical differences between treatment conditions and the control were assessed by Chi square, whereas comparisons between CNQX and CNQX + anisomycin were

assessed with an unpaired t test (two-tailed). Surface GluA1 (sGluA1) was labeled and imaged as described previously (Sutton et al., 2006). Live-labeling (5 min) with an Oyster 550-conjugated rabbit polyclonal antibody against the lumenal domain of synaptotagmin 1 (syt-lum; 1:100, Synaptic Systems) was used for assessing presynaptic function. Prior to labeling, neurons were treated with 2 μM TTX for 15 min to isolate spontaneous neurotransmitter release. Synaptic terminals were identified in the same samples with either a mouse monoclonal antibody against bassoon (1:1000, Stressgen) or a guinea pig polyclonal vglut1 antibody SCH772984 chemical structure (1: 2500, Chemicon). For BDNF staining, cells were fixed on ice for 30 min with 4% paraformaldehyde (PFA)/4% sucrose in phosphate buffered saline with 1 mM MgCl2 and 0.1 mM CaCl2 (PBS-MC), permeabilized (0.1% Triton

X in PBS-MC, 5 min), blocked with 2% bovine serum albumin (BSA) in PBS-MC for 30 min, and labeled with a rabbit polyclonal 5-Fluoracil antibody against BDNF (Santa Cruz, 1:100). For colabeling of dendrites, axons, and astrocytes, respectively, we used the following: a mouse monoclonal antibody against MAP2 (Sigma, 1:5000), a pan-axonal neurofilament mouse monoclonal antibody cocktail (1:8000, clone SMI-312, Covance), and a mouse Thymidine kinase monoclonal antibody against GFAP (Sigma, 1:1000). Secondary detection was achieved with Alexa 488-, 555-, or 635-conjugated goat anti-rabbit or goat anti-mouse antibodies for 60 min at RT. All imaging was performed on an inverted Olympus FV1000 laser scanning confocal microscope with identical acquisition parameters for each treatment condition. Image analysis was performed on maximal intensity z-projected images. For analysis of

sGluA1 or syt-lum staining, a “synaptic” particle was defined as occupying greater than 10% of the area defined by a PSD95 or vglut1/bassoon particle. Analysis was performed with custom written analysis routines for ImageJ. Statistical differences were assessed by ANOVA, then by Fisher’s LSD post-hoc tests. Stable microperfusion was achieved by use of a dual micropipette delivery system, as described (Sutton et al., 2006). A cell-impermeant fluorescent dye (either Alexa 488 or Alexa 555 hydrazide, 1 μg/ml) was included in the local perfusate to visualize the affected area. In all local perfusion experiments, the bath was maintained at 37°C and continuously perfused at 1.5 ml/min with HBS. For analysis, the size of the treated area was determined in each linearized dendrite based on Alexa 488/555 fluorescence integrated across all images taken during local perfusion. Adjacent nonoverlapping dendritic segments, 25 μm in length, proximal and distal to the treated area were assigned negative and positive values, respectively.

In contrast, diazepam had no significant effects

on the frequency of mIPSCs (Ctrl: 11.8 ± 1.99 Hz; DZ: 12.8 ± 2.4 Hz; DZ+Flu: 11.9 ± 2.1 Hz, n = 8; p = 0.139; one-way RM ANOVA) (Figures 5A and 5C). We then examined whether the impaired CF synapse elimination in GAD67+/GFP mice ABT-263 cell line is rescued by chronic application of diazepam. Elvax containing 0.5 mM diazepam or vehicle was implanted to the cerebellum of GAD67+/GFP mice at P10. Then, CF innervation was examined at P22–P31. In 49% of PCs (22/45) from vehicle-treated mice, CF-EPSCs with two or three discrete steps were elicited (Figure 5D) as in untreated GAD67+/GFP mice (Figure 2A). By marked contrast, large CF-EPSCs with single steps were elicited in 77% of PCs (50/65) in diazepam-treated GAD67+/GFP mice (Figure 5D).

Summary data show significant difference in the frequency distribution of PCs between the two groups (p = 0.011) (Figure 5D). Basic properties of CF-EPSCs were similar (Table S2), indicating that kinetics of CF-EPSCs was not altered by diazepam. http://www.selleckchem.com/products/sorafenib.html When the diazepam application was started at P17, many PCs remained innervated by multiple CFs at P22–P31 (Figure 5E) with no significant difference between the diazepam- and vehicle-treated groups (p = 0.164). Taken together, these results strongly suggest that GABAergic inhibitory tone from P10 to P16 within the cerebellum is an important factor that regulates crotamiton developmental CF synapse elimination. Next, we investigated which type of GABAergic synapses in the cerebellum is crucial for CF synapse elimination. We first evaluated GABAergic transmission onto GCs. Spontaneous IPSCs (sIPSCs)

were recorded in control and GAD67+/GFP GCs at P10–P13. Neither the amplitude (control: 50 ± 4.3 pA, n = 20; GAD67+/GFP: 40 ± 3.7 pA, n = 12; p = 0.138) nor the frequency (control: 2.2 ± 0.4 Hz, n = 20; GAD67+/GFP: 1.5 ± 0.2 Hz, n = 12; p = 0.179) of sIPSC was different between control and GAD67+/GFP GCs, indicating that GABAergic transmission onto GCs is not altered in GAD67+/GFP mice during the GAD67-sensitive period of CF synapse elimination. To narrow down the candidate GABAergic synapses responsible for CF synapse elimination, we generated conditional GAD67 knockout mice by intercrossing GAD67 floxed mice (Obata et al., 2008) with a D2CreN line (GluD2+/Cre) whose Cre gene was driven under the control of the GluD2 promotor (Hashimoto et al., 2011 and Yamasaki et al., 2011). Although GluD2 was previously thought to be a PC specific molecule, a recent study has demonstrated a low level of GluD2 expression in molecular layer interneurons (Yamasaki et al., 2011). Accordingly, in the D2CreN mice, Cre gene is expressed in not only PCs but also SCs and BCs, but is undetectable in other cell types (Yamasaki et al., 2011). Thus, in our conditional GAD67 KO mice, GAD67 was deleted from PCs, SCs and BCs (Figure S5), which we termed PC/SC/BC-GAD67 (−/−) mice.

We conducted further confirmatory analyses to ensure that the hierarchical regression was robust. Specifically, we subsampled the data in order to reverse the direction of eye movement differences across the conditions. In the original data set, there are more saccades in the Attention-High conditions than the Attention-Low conditions. In order to reverse the direction of this effect on a participant-wise basis, we sorted the trials within each condition according to the number of GW3965 saccades that occurred on that trial. In each Attention-High condition, we took all

scores below the 60th percentile. In each Attention-Low condition, we took all scores above the 40th percentile. As shown in Figure 3A, in the subsampled data, the number of saccades was much larger in the Attention-Low conditions than the Attention-High conditions (F(1,29) = 148.97, p < 0.001). In fact, CH5424802 clinical trial the absolute value of the difference between conditions was much larger in the subsampled data than in the original data. As in the original data, the main effect of Memory was significant

(F(1,29) = 4.44, p < 0.05) and the interaction was not significant (F(1,29) = 2.47, p = 0.13). The subsampled data were then subjected to the same analysis as the original data set. If the hierarchical regression is robust, the subsampled data should lead to similar conclusions: the effects of eye movements only have already been satisfactorily modeled, so any further classification of the data on the basis of eye movements should have no effect. Alternatively, if the activation presented in Figure 2 reflects the effects of eye movements, there should be a substantial reversal of these effects when the sub-sampled data are subjected to the same analysis. The same basic pattern of activation

seen in the main analysis (Figure 2) is also seen in the subsampled data (Figure 3). Although there is an expected slight reduction in the overall magnitude and extent of activation, which results from a reduction in power, the peak activations in parietal cortex are still clearly apparent. Time courses from the subsampled data (Figures 3C and 3D) closely resemble those obtained from the original data set. Similar conclusions were obtained when using the number of saccades between pictures as the measure of interest (Figure S4). There is a hint of residual effects of eye movements in early visual cortex (Figure 3, cool colors). Critically, however, activation of the dorsal attention network persisted despite these modest residual effects. These confirmatory analyses indicate that the hierarchical regression was robust and that the findings reported in Figure 2 cannot be attributed to the effects of eye movements. To identify regions associated with the retrieval of specific perceptual detail, we identified regions showing a significant main effect of Memory.