Quadruplicate biological replicates were collected from ten corti

Quadruplicate biological replicates were collected from ten cortical regions (4–8 individual layers), four hippocampal subfields, and three layers of the LGN (Table S1). Images of pre- and postlaser microdissection are shown in Figure S1. Microdissected tissue was collected directly into RLT buffer from the RNeasy Micro kit (QIAGEN Inc., Valencia, CA) supplemented with β-mercaptoethanol. Samples were volume adjusted with RLT Buffer to 75μl, vortexed, centrifuged, and frozen at −80°C. RNA was isolated for each brain region following the manufacturer’s directions. RNA samples were eluted in 14μl, and 1μl was run on the Agilent 2100 Bioanalyzer (Agilent

Technologies, Inc., Santa Clara, CA) using the Pico 6000 assay kit. Samples NSC 683864 in vivo were quantitated using the Bioanalyzer concentration output. The average RNA Integrity Number (RIN) of all 225 passed

experimental samples was 6.7. Sample amplification, labeling, and microarray processing were performed by the Rosetta Inpharmatics Gene Expression Laboratory (Seattle, WA). Samples passing RNA QC were amplified and profiled as described (Winrow et al., 2009) with a few modifications. Briefly, samples were amplified and labeled using a custom two cycle version, using two kits of the GeneChip HT One-Cycle cDNA Synthesis Kit from Affymetrix. Five nanograms of total RNA was added to the initial PD98059 in vivo reaction mix together with 250 ng of pBR322 (Invitrogen). As little as 2 ng was used in some cases where tissue was extremely limited. Hybridization was performed in three batches to GeneChip Rhesus Macaque Genome Arrays from Affymetrix containing 52,803 probesets/sequences. To control for batch effects, common RNA pool control samples were amplified and hybridized in each batch (3 replicates per 96-well batch). Profile quality was

assessed using standard Affymetrix quality control metrics as well as by PCA. A total of 8 outliers were identified, and these samples were recollected and hybridized successfully. A total of 258 samples passed sample QC, including 225 experimental samples and 33 control samples. The experimental samples include two male and two female profiles for each region (except three missing samples; Table S1). The data discussed in this publication were deposited in NCBI’s Gene Expression Omnibus (Edgar et al., 2002) and are accessible Thalidomide through GEO Series accession number GSE31613. Each batch was normalized within itself using RMA (Irizarry et al., 2003), and batch effects were removed by subtracting the difference for each probe between controls of one batch from the controls of each other batch. Following this correction, no correlation with batch was observed among all samples within the four primary principal components which explain approximately 40% of the cumulative variance (Figures S2A and S2B; data not shown). ANOVA, principal component and agglomerative clustering was performed using Matlab2007a.

Therefore, the function of UNC79 in mammalian brain may perhaps b

Therefore, the function of UNC79 in mammalian brain may perhaps be to control the stability and trafficking of UNC80, and to determine the localization of the NALCN complex with its various isoforms, thereby indirectly affecting NALCN’s function in various neuronal compartments. In mice and humans, NALCN is expressed in the brain, spinal cord, heart, and pancreas, with the highest mRNA expression levels detected in the brain. In the brain and spinal cord, learn more NALCN mRNA is widely expressed, and found in essentially all the neurons (Lu et al., 2007). The expression pattern in the nervous system suggests some fundamental

roles for NALCN, and three basic cellular functions are discussed here. The basal Na+ leak current (IL-Na) is small in most neurons, representing about 10-20 pA of whole cell current at −70 mV

in cultured mouse hippocampal neurons (Lu et al., 2007). Because of its small size, IL-Na is perhaps best measured as the change of holding currents when extracellular Na+ concentration ([Na+]e) is lowered from high (140 mM) to low (14 mM) concentrations under voltage clamping (Raman and Bean, 1997). In the cultured mouse hippocampal neurons, IL-Na can be partially blocked by TTX (∼18%, presumably contributed by the window current through NaV) and by 2 mM Cs (∼10%, likely through HCN channels). The remaining Sirolimus mw ∼72% current can be almost completely blocked by genetic deletion of Nalcn

or by applying the non-specific NALCN blocker, Gd3+ (10 μM) ( Lu et al., Sodium butyrate 2007). The complete elimination of IL-Na by blocking NaVs, HCNs, and NALCN suggests that, in these neurons, these three channels make the major contributions to the resting Na+ leak current, with NALCN having the largest (∼70%) contribution. This is somewhat surprising given that some of the 26 mammalian TRP channels are also found in neurons and, when expressed heterologously, they are open at RMPs ( Ramsey et al., 2006). Many of the TRP channels are used for sensory detection and it’s not clear whether they contribute basal Na+ conductance. The RMP of the Nalcn knockout hippocampal neurons is approximately 10 mV more hyperpolarized than that of wild-type neurons, and is less sensitive to change in [Na+]e. Conversely, overexpression of NALCN leads to a depolarization of ∼20 mV of the RMP ( Lu et al., 2007). In the snail Lymnaea stagnalis, knocking down NALCN in a pacemaker neuron (RPeD1) also leads to an ∼15 mV hyperpolarization of the RMP ( Lu and Feng, 2011). These studies suggest that NALCN is a major player in determining the influence of extracellular Na+ on a neuron’s basal excitability. Like Na+ and K+, extracellular Ca2+ also influences the basal neuronal excitability in many brain regions. The systemic [Ca2+] of the body (∼1.

Remarkably, the opposing effects of NA upon spontaneous and evoke

Remarkably, the opposing effects of NA upon spontaneous and evoked inhibition were both due to noradrenergic elimination of cartwheel cell spontaneous spiking. Under control conditions, cartwheel synapses were tonically depressed by background spiking activity. By shutting off spontaneous spiking, NA relieved cartwheel synapses from depression and thereby enhanced glycine Volasertib release in response to parallel fiber stimulation. This mechanism for neuromodulation, in which synaptic output is indirectly controlled through modulation of spontaneous activity, may have distinct advantages over direct regulation of presynaptic release probability in spontaneously firing cells. We examined whether NA affects integration

of excitatory and inhibitory signals conveyed through the molecular layer circuitry of the DCN. Whole-cell voltage-clamp recordings were acquired from fusiform cells in acute slices of mouse brainstem and synaptic currents were recorded in response to activation of parallel fibers by an extracellular stimulating electrode positioned in the

DCN molecular layer (Figure 1A). Single stimuli typically elicited weak excitatory currents check details and small or undetectable inhibitory currents (see first stimulus; Figure 1B, top). Because parallel fibers exhibit strong short-term facilitation (Roberts and Trussell, 2010 and Tzounopoulos et al., 2004), brief stimulus trains (three stimuli at 20 Hz) were applied to recruit robust parallel fiber activity. When fusiform cells were clamped at −60 mV, intermediate to the reversal potentials below for Cl− conductances (−84 mV) and excitatory conductances (∼0 mV), each stimulus elicited a sequence of inward current followed

closely by outward current (Figure 1B), characteristic of direct activation of excitatory fibers followed by feed-forward recruitment of inhibitory inputs (Mittmann et al., 2005 and Pouille and Scanziani, 2001). Both inward and outward components of the responses were larger for the second and third parallel fiber stimuli due to facilitation of excitatory inputs onto both fusiform and cartwheel cells (Roberts and Trussell, 2010). Consistent with activation of disynaptic inhibition, inward and outward components of the evoked responses were largely abolished by application of NBQX (Figure 1C). When identical stimulus trains were applied in the presence of 10 μM NA, we observed a significant enhancement of the outward components of evoked currents in response to the second and third stimuli (Figures 1B, middle, and 1D; measured as total outward charge, see Experimental Procedures; stim 2 control: 701 ± 246 pA∗ms, NA: 1809 ± 561 pA∗ms, p < 0.05, n = 6; stim 3 control: 596 ± 203 pA∗ms, NA: 1680 ± 286 pA∗ms; p < 0.01, n = 6). This effect could be clearly visualized by subtracting average control responses from average currents recorded in NA (Figure 1B, bottom).

Even worse, it turned out that motor maps derived from the same s

Even worse, it turned out that motor maps derived from the same species

by different investigators could differ considerably. It was also observed that motor maps are not even entirely consistent Selleckchem Fulvestrant within experiments, an observation referred to “functional instability of cortical motor points” by Sherrington. Finally, it was found that musclelotopy captures the complexity of cortical motor organization only partially (Schieber, 2001) and that motor cortex might contain multiple entirely different maps. In particular, when long and intense stimulation trains are used, one can evoke from single motor cortical sites complex, “goal-directed” motor behaviors (Graziano et al., 2002). As these movements include sequences of very different muscle activation patterns, they require some kind of remapping of motor output during behavior. Behaviors map in an orderly fashion onto motor cortex and are organized according to “ethological” categories, i.e., defensive behaviors, reaching behaviors, etc. Ultimately, investigators started to integrate cytoarchitectonic, connectional, recording, and lesion data in their concepts of cortical localization, but—while it greatly expanded our knowledge of cortical circuitry—it also led to novel disagreements and an even wider variety PI3K Inhibitor Library of cortical partitioning schemes. This has led to a Babylonian confusion about

how to label cortical areas. Thus, two studies published in this issue of Neuron report data from exactly the same area in rodent cortex, but they refer to it under different names, namely as vibrissae primary motor cortex (vM1; Hill et al., 2011) or frontal orienting field (FOF; Erlich et al., 2011). If there were just two names for this area, we would probably deal with it, but the reality is that this exact same piece of cortex has also been referred to as anteromedial cortex, dorsomedial prefrontal cortex, medial precentral cortex, frontal eye field (FEF), vMC (vibrissa motor

cortex), agranular medial area (AgM), frontal area 2 (F2), and secondary motor cortex (M2). This cacophony of names fundamentally impairs our ability to communicate our findings. There is hope, however. First, investigators have taken up the challenge posed by cortical complexity. Specifically nearly as reported in this issue, Hill et al. (2011) and Erlich et al. (2011) performed sophisticated recording, blocking, and deafferentation experiments in rats. Perhaps most importantly, the researchers overcame the temptation to be original and performed experiments very similar to those that had been done before in other cortical areas and species. As discussed in depth below, the results reveal both intriguing similarities and crystal-clear differences between cortical areas; collectively, the experiments make one feel that we are on the road of clarification about motor cortices.

5 on the 1 octave discrimination within 3 days of training, where

5 on the 1 octave discrimination within 3 days of training, whereas the High and Control Groups took >8 days to reach the same level of performance [ Figure 2B, days to reach d′ = 0.5, Low: 2.8 ± 0.8, High: 8.2 ± 2.3, Control: Docetaxel research buy 10 .0 ± 2.6, analysis of variance (ANOVA) F(2,14) = 4.14, p = 0.043]. The Low Group performed significantly better than the other two groups on the final 2 days of training on the easy frequency discrimination task [d′ discrimination of all three distracter tones by Low, High, and Control groups, F(2,14) = 4.94, p = 0.027, repeated-measures ANOVA] (see Table S1 available online). After 6 days of training, the Control Group was unable to

discriminate the target tone from any of the three distracter tones ( Figure 2E). In contrast, the Low Group was able to discriminate all three distracters from the target ( Figure 2C). This result confirms our prediction that an exaggerated representation of low-frequency tones would improve learning of a low-frequency discrimination task. The High Group was not able to discriminate the target from the two lowest PF-01367338 order distracters (0.5 and 1.0 octave higher), but was able to discriminate the target from the highest distracter (2.4 octaves higher; Figure 2D). The highest distracter was only 1 octave below the 19 kHz tone that was paired with NBS. We analyzed physiological data in the untrained rats that experienced NBS paired with 19 kHz tones (Figure 1)

and found that the pairing caused an increased cortical response to the 2.4 octave distracter (9.5 kHz) 1–20 days after the end of NBS-tone pairing (45 ± 3 versus 32 ± 3 percent cortex, p = 0.029). An exaggerated representation of high tones is the most likely reason that the High Group was able to learn to reject the 2.4 octave distracter more quickly than the Control Group. The results of Experiment 1 demonstrate Casein kinase 1 that NBS-tone pairing

before training can enhance tone frequency discrimination learning. This supports the hypothesis that map plasticity is a key substrate of improved discrimination learning. In Experiment 2 we tested whether NBS-low tone pairing could improve discrimination in rats that had already learned to discriminate low-frequency tones. Twelve rats were trained to perform the low-frequency discrimination task for 10 days and then tested on the same task for 10 additional days (Figure 3A). After mastering the frequency discrimination task (Figure 3B), rats were placed on full feed with no behavioral testing for 20 days. For 3 hr each day, rats were exposed to 300 low-frequency (2 kHz) tones. For rats in the Pretrained Low Group, the low tone was paired with NBS (Figure 3A, red). Rats in the Pretrained Control Group did not experience any stimulation (Figure 3A, green). There was no difference in the discrimination abilities of the Pretrained Low Group compared to rats in the Pretrained Control Group [Figure 3C; F(1,11) = 0.8898, p = 0.72].

Figure 3 summarizes the major long-range inputs onto the direct a

Figure 3 summarizes the major long-range inputs onto the direct and indirect pathways in the region of dorsal striatum diagrammed in Figures 2E–2G. Since our helper virus did not allow for direct visualization of the number of starter cells, we only report the percentage of total input provided by any given brain region. Inputs were normalized across each animal to prevent mice with many labeled inputs from overly biasing total input proportion. Only inputs that were detected in at least three mice total (across all mouse types) were included

for display. For D1R-Cre mice, 162 ± 24 transsynaptically labeled cells were detected per animal outside of the striatum (n = 9, mean ± 1 SEM); for EPZ-6438 cell line D2R-Cre mice, 207 ± 29 cells per animal

were detected LBH589 in vitro (n = 10, p = 0.3 for D1R versus D2R by two-tailed t test). For WT mice, no cells were detected (n = 6). Corticostriatal neurons comprised the majority of long-range inputs onto both pathways (61.1% of total inputs onto the direct pathway, 69.6% onto the indirect pathway). These inputs arose primarily from the somatosensory and motor cortices, but there was also significant input from prefrontal cortical structures and limbic structures known to project directly into striatum. Dorsolateral striatum is known to receive primarily somatosensory and motor inputs (Künzle, 1975, Liles and Updyke, 1985 and McGeorge and Faull, 1989), while dorsomedial striatum is thought to receive a higher proportion of frontal and limbic inputs (Goldman and Nauta, 1977, McGeorge and Faull, 1989 and Ragsdale and Graybiel, 1981). The slight lateral bias of the injection site (Figure 2F) likely explains the relative proportion of inputs from various cortical structures. Thalamus provided the majority of the remaining inputs into striatum (22.0% of total inputs onto the direct pathway, 25.5% of total inputs onto the indirect pathway).

Although the dorsal striatum receives input from a large number of thalamic nuclei, the majority of thalamostriatal input arose from the medial dorsal and parafascicular nuclei. These inputs correspond well with previous experiments using traditional retrograde tracers to label thalamic inputs to the region of dorsal striatum that we targeted (Erro et al., ADAMTS5 1999, Pan et al., 2010, Schwab et al., 1977 and Smith et al., 2009). We first wished to determine whether there were differences in the excitatory drive onto the direct versus indirect pathway, so we examined the strength of cortical glutamatergic input to D1R- versus D2R-expressing cells. Representative images from three cortical structures (primary sensory [Figures 4A and 4B] and motor cortices [Figures 4C and 4D], as well as the orbitofrontal cortex [Figures 4E and 4F]) demonstrate the quality of label obtained via monosynaptic tracing.

The anatomical connections of IT cortex also support a role in ob

The anatomical connections of IT cortex also support a role in object recognition and visual memory (Figure 2).

IT cortex lies at the pinnacle of the ventral cortical visual processing stream and its neurons receive convergent projections from many visual areas at lower ranks, thus affording integration of information from a variety of visual submodalities (Desimone et al., 1980 and Ungerleider, 1984). As noted above, IT cortex is also reciprocally connected with MTL structures that are critical for acquisition of declarative memories (Milner, 1972, Mishkin, 1982, Murray et al., 1993 and Squire and Zola-Morgan, 1991). Finally, the visual response properties of IT neurons, which have been explored in much detail over the past 40 years, also exhibit features

SAR405838 that suggest a role in object recognition and visual memory (for review see Gross et al., 1985 and Miyashita, 1993). Most importantly, IT neurons are known to respond selectively to complex objects—often those with some behavioral significance to the GSK2118436 nmr observer, such as faces (Desimone et al., 1984 and Gross et al., 1969). Based on this collective body of evidence, it would seem that IT cortex is unique among visual areas and strongly implicated as a storage site for long-term associative memories. Yet, there are reasons to suspect that associative neuronal plasticity may be a general property of sensory cortices. Evidence for this comes in part from functional brain imaging studies that have found learning-dependent activity changes in early cortical visual areas (e.g., Shulman et al., 1999 and Wheeler et al., 2000). Motivated by these findings, Schlack and Albright (2007) explored the possibility that associative learning

might influence response properties in the middle temporal visual area (area MT), which occupies a relatively early position in the cortical visual processing hierarchy (Ungerleider and Mishkin, 1979). In an experiment that represents a simple analog to the paired-association learning studies of Sakai and Miyashita (1991) and Messinger et al., 2001 and Schlack and almost Albright, 2007 trained monkeys to associate directions of stimulus motion with stationary arrows. Thus, for example, monkeys learned that an upward-pointing arrow was associated with a pattern of dots moving in an upward direction, a downward arrow was associated with downward motion, etc. (Figures 3A and 3B). Moving stimuli were used for this training because it is well known that such stimuli elicit robust responses from the vast majority of neurons in cortical visual area MT (Albright, 1984).

We next asked if increased miniature events are necessary for cpx

We next asked if increased miniature events are necessary for cpx mutant terminal overgrowth. We reduced miniature NT by expressing Sotrastaurin either

a dominant-negative glutamate receptor subunit (UAS-dGluRDN) ( Schmid et al., 2006) in postsynaptic muscles or an RNAi against vglut (RNAi-Vglut) in the presynaptic MNs of cpx mutants ( Figures 4F and 4G) and controls ( Figures S5D and S5E). Both manipulations did not significantly alter evoked NT in cpx mutants but did decrease miniature NT ( Figures 4F–4I). In both conditions, we found that the aberrant synaptic terminal area and bouton size indexes of cpx mutants were suppressed ( Figures 4J, 4K, 4Q, and 4R). Thus, the inhibition of miniature NT suppressed the terminal overgrowth of cpx mutants while the depletion

of evoked NT did not. Therefore, increased miniature neurotransmission, as found in complexin mutants, is sufficient to promote synaptic terminal growth. cpx mutants had opposing check details synaptic morphological changes to vglutMN and iGluRMUT mutants. This was most apparent in the bidirectional effect upon bouton size, with vglutMN and iGluRMUT mutants having an increase in the proportion of small boutons and cpx mutants oppositely having a decreased fraction of these boutons. During terminal development, new synaptic boutons are added and then expand and may also be eliminated ( Koch et al., 2008 and Zito et al., 1999). A defect in one or more of these steps could potentially result in the changes to bouton sizes we observed

when miniature NT was altered. We sought therefore to visualize the development of individual synaptic boutons by time-lapse live imaging through the transparent cuticle of intact larvae. To do this, we utilized the LexA binary system to express a membrane-localized GFP in the presynaptic terminals of both control and Non-specific serine/threonine protein kinase miniature NT mutants. Beginning 24 hr after hatching, we anesthetized animals every 24 hr for 4 days during larval development and imaged their synaptic terminals, returning them to food media between imaging periods. Using this technique, we found that new synaptic boutons formed continuously throughout the imaging period in control and NT mutant backgrounds at the same rate (Figures S6A and S6B). In control animals, 94.4% (34/36) of newly formed small boutons (<2 μm2) then became progressively larger over time to become typical-sized boutons (>2 μm2) during the imaging period (Figures 5A and 5G). This expansion in size was not perturbed by inhibiting evoked NT using TeTxLC (Figures 5B and 5G). However, in iGluRMUT mutants, where miniature NT was reduced, we found that the enlargement of small boutons was severely retarded compared to iGluRWT animals ( Figures 5C, 5D, and 5H) and only 19.6% (10/51) of small boutons ever expanded to become typical-sized boutons.

3 ± 11 4

years) and 1378 healthy controls (54 0% female;

3 ± 11.4

years) and 1378 healthy controls (54.0% female; mean age 51.1 ± 17.0 years) for which genotype data at the four FEZ1 SNPs were available ( Table S1A). No significant results were detected on susceptibility to schizophrenia for each of the four FEZ1 SNPs, which is consistent with the result from the ZHH cohort ( Table S1A). The platform used to genotype GAIN (Affymetrix 6.0 chip) did not include the DISC1 Ser704Cys marker but has a perfect proxy for this SNP (rs1754605; r2 = 1.0). We then performed a backward selleck screening library stepwise regression to test for an interaction between the proxy SNP for DISC1 Ser704Cys and FEZ1 rs12224788 ( Figure 6C). As this approach was to serve as a replication of the findings in the ZHH data set, we included only the FEZ1 SNP with statistical evidence of epistasis (FEZ1 rs12224788) in the GAIN sample regression model ( Table S1B). Variables retained in the best fit model included FEZ1 genotype (Beta = 0.72; p = 0.041), DISC1 genotype (Beta = 0.53; p = 0.044), and the interaction term FEZ1 × DISC1 (Beta = −0.45; p = 0.039). While the significant interaction is consistent with results from the ZHH data set, the Beta term is negative, suggesting a somewhat different pattern of

interaction in the GAIN sample as compared with the ZHH sample. Specifically, χ2 analyses revealed only a trend-level association for the C allele at FEZ1 PD0332991 price rs12224788 in DISC1 Ser homozygotes (χ2 = 1.53; df = 1; p = 0.12; OR = 1.2) and a significant association for the FEZ1 GG genotype in the context of a DISC1 Cys background (χ2 = 2.83; df = 1; p = 0.05; OR = 0.77; Figure 6B). While not identical, this pattern of risk association related to the interaction is consistent with that found through in the ZHH sample. We also performed a series of χ2 tests in the same way to test for a potential interaction between our NDEL1 risk SNP (rs1391768) ( Burdick et al., 2008) and each of the four FEZ1 SNPs using the ZHH sample. We carried out four separate χ2

analyses with one for each FEZ1 SNP, while conditioning the sample on NDEL1 rs1391768 status. The results from these analyses provided no significant evidence of interaction among these four FEZ1 SNPs and NDEL1 rs1391768 (all p > 0.10; Figure S6D and data not shown). Taken together, these genetic interaction results from clinical cohorts mirror the biochemical and cell biological findings of a synergistic interaction between FEZ1 and DISC1, but not between FEZ1 and NDEL1, in regulating neuronal development in the animal model. Cumulative evidence supports a significant neurodevelopmental contribution to the pathophysiology of schizophrenia and other major mental disorders (Lewis and Levitt, 2002, Rapoport et al., 2005 and Weinberger, 1987), yet underlying molecular mechanisms are far from clear. DISC1 has emerged as a general risk factor for schizophrenia, schizoaffective disorder, bipolar disorder, major depression, autism, and Asperger syndrome (Chubb et al., 2008 and Muir et al., 2008).

New vaccine introductions were seen as intrinsically positive, to

New vaccine introductions were seen as intrinsically positive, to such an extent that some study participants felt that their addition per se strengthened the health system in a general sense. “I think any new antigen reinforces [the] routine vaccination programme because Libraries mothers know their children are better protected. Respondents felt that the new vaccines would lead to a reduction in disease and would increase the public’s trust in the health system. Staff training in preparation for the introductions was viewed

overwhelmingly positively. Some participants explained that it acted as a refresher, allowing staff to update their vaccination skills, selleckchem e.g. cold chain management, as well as informing them about the new vaccine. There was generally no impact on disease surveillance systems overall. However in some countries positive effects were reported, namely Cameroon, Mali and Kenya, where surveillance staff capacity had reportedly

been enhanced. In addition, in Mali (Men A) case-based surveillance of meningitis was introduced. This overall lack of impact may be because the development and strengthening of surveillance systems was part of broader developments within the health system and as such, were not tied specifically to individual vaccine introductions. Study participants felt that the effect of the new vaccine introductions Ceritinib ic50 on adverse events following immunisation (AEFI) reporting was positive, though

limited. In Ethiopia and Mali, the AEFI surveillance systems had been strengthened, with training and specific communication for health workers on how to identify and respond to AEFIs for the new vaccine and the strengthening of national and regional committees for surveillance of AEFIs. In several countries (particularly Kenya, Ethiopia and Mali for Men A) a lot of attention was placed on creating awareness of potential AEFIs. These countries introduced vaccines with particular safety concerns; Idoxuridine Kenya was the first GAVI-eligible country to introduce the preservative-free PCV10 vaccine, shortly followed by Ethiopia, whilst Mali introduced a completely new Men A vaccine [21]. However despite overwhelming reports of enhanced awareness of AEFIs, this did not lead to a change in the number of AEFIs reported by health facilities, for any vaccine. The impact of the new vaccines on domestic and external financing was viewed positively. Domestic funding for vaccines was increased, albeit only for GAVI co-financing in most cases; operational funds were generally reported to have remained unchanged. Some interviewees believed that GAVI co-financing encouraged a sense of national ownership although concerns were also expressed regarding financial sustainability.